Methods and apparatus for predicting favored wireless service areas for drones

ABSTRACT

Methods and apparatus for predicting favored wireless service areas for drones are disclosed. A controller for a drone includes a service area identifier to identify favored wireless service areas during a flight of the drone. The favored wireless service areas are predicted by a model developed remotely from the drone. The controller also includes a service area selector to select one of the favored wireless service areas during the flight. The controller also includes a route manager to adjust a flight path of the drone during the flight based on the selected one of the favored wireless service areas.

FIELD OF THE DISCLOSURE

This disclosure relates generally to methods and apparatus fordetermining favored wireless service areas and, more specifically, tomethods and apparatus for predicting favored wireless service areas fordrones.

BACKGROUND

A drone traveling through an airspace during a flight may exchangecontrol data (e.g., data associated with control of the flightoperations and/or the route of the drone) with a ground controller viaone or more cell(s) of one or more cellular base station(s) locatedwithin the airspace. Failure to maintain a communication channel betweenthe drone and the ground controller via the cell(s) of the cellular basestation(s) may lead to a lapse of control and/or a complete loss ofcontrol over the flight operations and/or the route of the drone.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a known environment of use in which a drone travelingthrough an airspace attempts to adjust its route based on locations offavored wireless service areas determined in real time during flight ofthe drone.

FIG. 2 illustrates an example environment of use in which an exampledrone traveling through an example airspace adjusts its route based onfavored wireless service areas identified and selected in real timeduring flight of the drone.

FIG. 3 is a block diagram of an example implementation of the server ofFIG. 2 constructed in accordance with the teachings of this disclosure.

FIG. 4 is an example three-dimensional representation including anexample grid built by the example grid builder of FIG. 3.

FIG. 5 illustrates an example correlation grid having example testlocations for training a model.

FIG. 6 is a block diagram of an example implementation of the drone ofFIG. 2 constructed in accordance with the teachings of this disclosure.

FIG. 7 illustrates an example environment of use in which the exampleprediction engine of the example drone of FIGS. 2 and/or 6 may implementa model developed by the example model developer of the example serverof FIGS. 2 and/or 3 to identify and select favored wireless serviceareas for an example route of the drone in real time.

FIG. 8 is a flowchart representative of example machine readableinstructions that may be executed at the example server of FIGS. 2and/or 3 to develop a model to predict favored wireless service areasfor a drone.

FIG. 9 is a flowchart representative of example machine readableinstructions that may be executed at the example server of FIGS. 2and/or 3 to develop a model using correlation grids to predict favoredwireless service areas for a drone.

FIG. 10 is a flowchart representative of example machine readableinstructions that may be executed at the example drone of FIGS. 2 and/or6 to adjust the drone based on favored wireless service areas identifiedand selected in real time during flight of the drone.

FIG. 11 is a block diagram of an example processor platform structuredto execute the instructions of FIGS. 8 and 9 to implement the exampleserver of FIGS. 2 and/or 3.

FIG. 12 is a block diagram of an example processor platform structuredto execute the instructions of FIG. 10 to implement the example drone ofFIGS. 2 and/or 6.

Certain examples are shown in the above-identified figures and describedin detail below. In describing these examples, identical referencenumbers are used to identify the same or similar elements. The figuresare not necessarily to scale and certain features and certain views ofthe figures may be shown exaggerated in scale or in schematic forclarity and/or conciseness.

DETAILED DESCRIPTION

As used herein, a “wireless service area” is defined to be a geographicarea in which a drone is able to wirelessly access a network. A wirelessservice area can be a cell serviced by a base station in a wirelesscommunication system, and/or an area serviced by a wireless access pointoperating under any past, present or future wireless communicationprotocol such as Wi-Fi. As used herein, a “favored wireless servicearea” is defined to be a wireless service area (as defined above) whichis more likely (relative to one or more other wireless service areasavailable to a drone) to maintain a communication channel between adrone in the wireless service area and a remote controller of the drone.The remote controller may be, for example, a ground based device.

Known methods and/or processes exist for selecting a flight path of adrone in real time so as to traverse one or more favored wirelessservice area(s) during flight of the drone. FIG. 1 illustrates a knownenvironment of use 100 in which a drone traveling through an airspaceattempts to adjust its route based on locations of favored wirelessservice areas determined in real time during flight of the drone. Theenvironment of use 100 of FIG. 1 includes a first drone 102 and a seconddrone 104. The first drone 102 and the second drone 104 of FIG. 1 arerespectively shown traveling through an airspace 106 of the environmentof use 100.

The airspace 106 of FIG. 1 includes a first cellular base station 108, asecond cellular base station 110, a third cellular base station 112, afourth cellular base station 114, a fifth cellular base station 116, asixth cellular base station 118, a seventh cellular base station 120,and an eighth cellular base station 122. Respective ones of the cellularbase stations 108, 110, 112, 114, 116, 118, 120, 122 of FIG. 1 serviceassociated cell(s). Each cell has the potential to provide acommunication channel between a drone (e.g., the first drone 102, thesecond drone 104, etc.) traveling within its corresponding area of theairspace 106 of FIG. 1 and a server 124.

The server 124 of FIG. 1 is located remotely from the airspace 106. Theserver 124 implements, is operatively coupled to, and/or is incommunication with a ground controller capable of exchanging controldata with a drone (e.g., the first drone 102, the second drone 104,etc.) traveling within the airspace 106. Control data associated withthe ground controller may be exchanged between the server 124 and thedrone via one or more communication channel(s) associated with one ormore of the cellular base station(s) 108, 110, 112, 114, 116, 118, 120,122 located within the airspace 106. Failure to maintain a communicationchannel for exchanging the control data between the server 124 and thedrone may lead to a temporary and/or a complete loss of control over theflight operations and/or the route of the drone.

The first drone 102 and the second drone 104 of FIG. 1 respectivelyinclude a GPS receiver to collect and/or receive location datadetermined via GPS satellites 126. Location data collected and/orreceived via respective ones of the GPS receivers enables thecorresponding respective drone (e.g., the first drone 102 or the seconddrone 104) to determine, detect, and/or identify its location within theairspace 106.

Routes and/or travel paths of the drones traveling within the airspace106 may be based on such location data, and may further be based ondestination data known to and/or communicated to the drones. Forexample, the first drone 102 may travel along the first route 128 withinthe airspace 106 based on location data collected by the GPS receiver ofthe first drone 102, and further based on destination data known toand/or communicated to the first drone 102. The second drone 104 maytravel along the second route 130 within the airspace 106 based onlocation data collected by the GPS receiver of the second drone 104, andfurther based on destination data known to and/or communicated to thesecond drone 104.

The first drone 102 of FIG. 1 includes a directional antenna having anassociated directional communication beam 132 that may be steered and/orpositioned in a desired direction. During flight, the first drone 102may steer the directional communication beam 132 in a multitude ofdifferent directions (e.g., sweeping the directional communication beamacross a full angular domain of 360 degrees) to measure and/or obtainone or more reference signal strength(s) associated with one or more ofthe cellular base station(s) 108, 110, 112, 114, 116, 118, 120, 122located within the airspace 106. Based on the measured and/or obtainedreference signal strength(s), the first drone 102 may determine whichcellular base station(s) 108, 110, 112, 114, 116, 118, 120, 122provide(s) the strongest communication channel(s)). The first drone 102may then attempt to adjust its position and/or its direction of travelrelative to the first route 128 of FIG. 1 to steer the directionalcommunication beam 132 of the first drone 102 toward the cellular basestation(s) 108, 110, 112, 114, 116, 118, 120, 122 providing thestrongest communication channel(s) (e.g., to traverse the correspondingfavored wireless service area(s)), thereby improving the likelihood thatthe first drone 102 will maintain a communication channel with theserver 124.

The second drone 104 of FIG. 1 includes an omnidirectional antennahaving an associated omnidirectional communication beam 134 thatradiates uniformly in all directions within a plane of the airspace 106.During flight, the second drone 104 may implement the omnidirectionalcommunication beam 134 to measure and/or obtain one or more referencesignal strength(s) associated with one or more of the cellular basestation(s) 108, 110, 112, 114, 116, 118, 120, 122 located within theairspace 106. Based on the measured and/or obtained reference signalstrength(s), the second drone 104 may determine which cellular basestation(s) 108, 110, 112, 114, 116, 118, 120, 122 provide(s) thestrongest communication channel(s)). The second drone 104 may thenattempt to adjust its position and/or its direction of travel relativeto the second route 130 of FIG. 1 to move the omnidirectionalcommunication beam 134 and/or, more generally, the second drone 104closer to the cellular base station(s) 108, 110, 112, 114, 116, 118,120, 122 providing the strongest communication channel(s) (e.g., totraverse the corresponding favored wireless service area(s)), therebyimproving the likelihood that the second drone 104 will maintain acommunication channel with the server 124.

The known method and/or process described above in connection with FIG.1 has several shortcomings. For example, in instances where the firstdrone 102 is traveling at a significant rate (e.g., a high rate) ofspeed, the first drone 102 may be unable to steer the directionalcommunication beam 132 in the desired multitude of different directions(e.g., a full sweep of a 360 degree angular domain) in real time. Thefirst drone 102 may accordingly be unable to sufficiently and/orcompletely measure and/or obtain the desired reference signalstrength(s) associated with the cellular base station(s) 108, 110, 112,114, 116, 118, 120, 122 in real time during flight. Determining whichcellular base station(s) 108, 110, 112, 114, 116, 118, 120, 122provide(s) the strongest reference signal strength(s) and/or thestrongest communication channel(s) may likewise prove to be a difficultprocessing task for the first drone 102 to perform in real time during ahigh-speed flight.

An additional shortcoming of the known method and/or process describedabove in connection with FIG. 1 lies in the fact that theomnidirectional communication beam 134 and/or the omnidirectionalantenna of the second drone 104 may encounter significant interferenceand/or noise which obscures and/or hinders the task of measuring and/orobtaining the reference signal strength(s) associated with the cellularbase station(s) 108, 110, 112, 114, 116, 118, 120, 122 in real timeduring flight. For example, interference and/or noise encountered by theomnidirectional communication beam 134 and/or the omnidirectionalantenna of the second drone 104 may negatively impact the accuracy ofreference signal strength(s) that may be measured and/or obtained. Insome instances, the negative impact of the interference and/or noise maybe severe enough to render the data corresponding to the measured and/orobtained reference signal strength(s) useless for the purpose ofdetermining and/or identifying the strongest reference signalstrength(s) and/or the strongest communication channel(s) for the seconddrone 104 in real time during flight.

Methods and apparatus disclosed herein overcome these shortcomings bypredicting favored wireless service areas for drones. Unlike the knownmethods and/or processes for determining favored wireless service areasdescribed above in connection with FIG. 1, example methods and apparatusdisclosed herein implement a model (e.g., a model developed offline by aserver) to predict favored wireless service areas for a drone travelingwithin an airspace. The model may be transmitted and/or uploaded to thedrone prior to the drone entering the airspace or, alternatively, whilethe drone is traveling within the airspace. The drone advantageouslyutilizes the favored wireless service areas identified by the model tomake, and/or to determine the need for, adjustments to the position or aroute of the drone in real time during a flight of the drone, andwithout the drone having to consume time and/or processing resourcesthat would otherwise be associated with measuring, obtaining, and/orevaluating reference signal strength data associated with the airspacein real time during flight.

FIG. 2 illustrates an example environment of use 200 in which an exampledrone 202 traveling through an example airspace 204 adjusts its routebased on favored wireless service areas identified and selected in realtime during flight of the drone 202. In the illustrated example of FIG.2, the airspace 204 includes a first example cellular base station 206,a second example cellular base station 208, a third example cellularbase station 210, a fourth example cellular base station 212, and afifth example cellular base station 214. In other examples, the airspace204 may include a different number of cellular base stations (e.g., onecellular base station, twenty cellular base stations, one hundredcellular base stations, etc.). In still other examples, the airspace 204may additionally or alternatively (e.g., relative to the cellular basestations 206, 208, 210, 212, 214 shown in FIG. 2) include one or morewireless access point(s) (e.g., an example wireless access point 216shown in FIG. 2) operating under one or more wireless communicationprotocol(s) (e.g., Wi-Fi).

Respective ones of the cellular base stations 206, 208, 210, 212, 214 ofFIG. 2 (and/or, if present, the wireless access points) serviceassociated cell(s). Each cell has the potential to provide acommunication channel between a drone (e.g., the drone 202 of FIG. 2)traveling within its corresponding area of the airspace 204 of FIG. 2and an example server 218. In the illustrated example of FIG. 2, theserver 218 is located remotely from the airspace 204 of FIG. 2. In otherexamples, the server 218 may be located within the airspace 204 of FIG.2. The example server 218 of FIG. 2 implements, is operatively coupledto, and/or is in communication with an example remote controller 220(e.g., a ground based controller) capable of exchanging control datawith a drone (e.g., the drone 202 of FIG. 2, or other drones) travelingwithin the airspace 204. Control data (e.g., control data associatedwith the remote controller 220) may be exchanged between the server 218of FIG. 2 and the drone via one or more communication channel(s)associated with one or more of the cellular base station(s) 206, 208,210, 212, 214 of FIG. 2 located within the airspace 204 of FIG. 2 and/orwith one or more wireless access point(s).

In the illustrated example of FIG. 2, the server 218 develops a model topredict favored wireless service areas for a drone (e.g., the drone 202of FIG. 2) traveling within the airspace 204 of FIG. 2. In someexamples, the server 218 of FIG. 2 develops the model by forming athree-dimensional representation of the airspace 204 and dividing thethree-dimensional representation of the airspace 204 into athree-dimensional grid. For example, as shown in FIG. 2, the server 218has divided an example three-dimensional representation 222 of theairspace 204 into an example three-dimensional, cubic grid 224 havingdimensions 4×4×4. In other examples, the grid 224 of FIG. 2 may haveother dimensions (e.g., 1×1×1, 10×10×10, 100×100×50, etc.) and/or othershapes (e.g., non-cubic and/or non-rectangular shapes) differing fromthe dimensions and/or shape of the grid 224 shown in FIG. 2.

The server 218 of FIG. 2 develops the model by identifying examplesampling locations 226 within the grid 224 of FIG. 2 from whichreference signal strength data associated with one or more of thecellular base station(s) 206, 208, 210, 212, 214 and/or reference signalstrength data associated with one or more wireless access point(s) is tobe collected and/or sampled. In some examples, the server 218 of FIG. 2instructs, controls, and/or commands one or more drone(s) (e.g., thedrone 202 of FIG. 2, or other drones) to travel to and collect referencesignal strength data from the sampling locations 226 within the grid224, and to transmit the collected reference signal strength data backto the server 218. In some such examples, the reference signal strengthdata received at the server 218 from the drone(s) may include and/or beassociated with sampling location data indicating the sampling location226 from which the reference signal strength data was collected, cellidentifier data indicating one or more cell(s) from which the referencesignal strength data was collected, cellular base station identifierdata indicating one or more of the cellular base station(s) 206, 208,210, 212, 214 from which the reference signal strength data wascollected, area identifier data indicating one or more area(s) fromwhich the reference signal strength data was collected, and/or wirelessaccess point identifier data indicating one or more wireless accesspoint(s) from which the reference signal strength data was collected.

The server 218 of FIG. 2 further develops the model by evaluating thereceived reference signal strength data to determine one or more favoredwireless service area(s) for each of the sampling locations 226 withinthe grid 224. In some examples, the server 218 may select the wirelessservice area having the greatest reference signal strength associatedwith a sampling location 226 to be the favored wireless service area forthe sampling location 226. In other examples, the server 218 may selectall wireless service areas associated with a sampling location 226 andhaving associated reference signal strengths that exceed a signalstrength threshold to be the favored wireless service areas for thesampling location 226.

The server 218 of FIG. 2 further develops the model by using the favoredwireless service area data associated with the sampling locations 226within the grid 224 to generate favored wireless service area dataassociated with other locations (e.g., locations other than the samplinglocations 226) within the grid 224. In some examples, the server 218 maygenerate the favored wireless service area data by implementing and/orexecuting a conditional random field (CRF) process. In some suchexamples, the server 218 may utilize the favored wireless service areadata associated with the sampling locations 226 as input for the CRFprocess.

In some examples, the server 218 of FIG. 2 may further develop the modelby receiving additional reference signal strength data associated withone or more example alternate location(s) 228 within the grid 224 (e.g.,locations other than the sampling locations 226) from one or moredrone(s) that may collect and/or sample such additional reference signalstrength data with or without having been instructed, controlled, and/orcommanded to do so by the server 218. In some such examples, theadditional reference signal strength data received at the server 218from the drone(s) may include and/or be associated with alternatelocation data indicating the alternate location 228 from which theadditional reference signal strength data was collected, cell identifierdata indicating one or more cell(s) from which the additional referencesignal strength data was collected, cellular base station identifierdata indicating one or more of the cellular base station(s) 206, 208,210, 212, 214 from which the additional reference signal strength datawas collected, area identifier data indicating one or more area(s) fromwhich the additional reference signal strength data was collected,and/or wireless access point identifier data indicating one or morewireless access point(s) from which the additional reference signalstrength data was collected.

In some examples, the server 218 of FIG. 2 further develops the model byevaluating the received additional reference signal strength data todetermine one or more favored wireless service area(s) for each of thealternate locations 228 within the grid 224. In some examples, theserver 218 may select the wireless service area having the greatestreference signal strength associated with an alternate location 228 tobe the favored wireless service area for the alternate location 228. Inother examples, the server 218 may select all wireless service areasassociated with an alternate location 228 and having associatedreference signal strengths that exceed a signal strength threshold to bethe favored wireless service areas for the alternate location 228.

In some examples, the server 218 of FIG. 2 further develops the model byusing the favored wireless service area data associated with thealternate locations 228 within the grid 224, together with the favoredwireless service area data associated with the sampling locations 226within the grid 224, to generate favored wireless service area dataassociated with other locations (e.g., locations other than thealternate locations 228 and other than the sampling locations 226)within the grid 224. In some examples, the server 218 may generate thefavored wireless service area data by implementing and/or executing theCRF process referenced above. In some such examples, the server 218 mayutilize the favored wireless service area data associated with thesampling locations 226 along with the favored wireless service area dataassociated with the alternate locations 228 as inputs for the CRFprocess.

In some examples, the model developed by the server 218 of FIG. 2 may beupdated and/or trained on an ongoing basis (e.g., a periodic basis, acontinuous basis, etc.). In some examples, the model may be updatedand/or trained based on, or in response to, the server 218 receivingupdated (e.g., new) reference signal strength data associated with thesampling locations 226 of the grid 224. The model may additionally oralternatively be updated and/or trained based on, or in response to, theserver 218 receiving updated (e.g., new) additional reference signalstrength data associated with the alternate locations 228 of the grid224. In some examples, the model may be updated and/or trained based onthe updated reference signal strength data and/or the updated additionalreference signal strength data received at the server 218 to determinethe favored wireless service area(s) associated with such updated data.

The server 218 of FIG. 2 transmits the model (e.g., the developed model)to the drone 202 of FIG. 2. In some examples, the server 218 maytransmit the model to the drone 202 prior to the drone 202 entering theairspace 204 of FIG. 2 (e.g., before beginning its journey, during acharging process, etc.). In other examples, the server 218 may transmitthe model to the drone 202 while the drone is in flight traveling withinthe airspace 204 of FIG. 2. For example, the server 218 of FIG. 2 maytransmit the model to the drone 202 of FIG. 2 via one or more of thecellular base station(s) 206, 208, 210, 212, 214, or one or more of thewireless access points located within the airspace 204 of FIG. 2.

The drone 202 of FIG. 2 includes a GPS receiver to collect and/orreceive location data determined via example GPS satellites 230.Additionally or alternatively, the drone 202 may determine location datavia base station triangulation based on signals recorded from three ormore of the cellular base stations 206, 208, 210, 212, 214. Locationdata collected and/or received by the drone 202 enables the drone 202 todetermine, detect, and/or identify its location within the grid 224and/or, more generally, within the airspace 204 of FIG. 2. A routeand/or travel path of the drone 202 may be based on such location data,and may further be based on destination data known to and/orcommunicated to the drone. For example, the drone 202 may travel alongan example route 232 within the grid 224 and/or, more generally, withinthe airspace 204 of FIG. 2 based on the location data collected by thedrone 202, and further based on destination data known to and/orcommunicated to the drone 202.

In the illustrated example of FIG. 2, the drone 202 includes adirectional antenna having an example associated directionalcommunication beam 234 that may be steered and/or positioned in adesired direction. In some examples, the drone 202 may adjust itsposition and/or the direction of travel of the drone 202 relative to theroute 232 of FIG. 2 to steer the directional communication beam 234 ofthe drone 202 toward one or more of the favored wireless service area(s)identified and/or determined via the above-described model received fromthe server 218 of FIG. 2, thereby improving the likelihood that thedrone 202 will maintain a communication channel with the server 218.These model-based adjustments can be made without measuring referencesignal strengths in flight, but instead based on the data pre-developedby the model. In such examples, the model advantageously enables thedrone 202 of FIG. 2 to make, and/or to determine the need for, suchadjustments in real time during a flight of the drone 202 without thedrone 202 having to consume time and/or processing resources that wouldotherwise be associated with measuring, obtaining, and/or evaluatingreference signal strength data associated with the airspace 204 of FIG.2 in real time during flight.

In other examples, the drone 202 of FIG. 2 may alternatively include anomnidirectional antenna having an associated omnidirectionalcommunication beam. In such other examples, the drone 202 may adjust itsposition and/or its direction of travel relative to the route 232 ofFIG. 2 to move the omnidirectional antenna and/or, more generally, thedrone 202 toward one or more of the favored wireless service area(s)identified and/or determined via the above-described model received fromthe server 218 of FIG. 2, thereby improving the likelihood that thedrone 202 will maintain a communication channel with the server 218.These model-based adjustments can be made without measuring referencesignal strengths in flight, but instead based on the data pre-developedby the model. In such other examples, the model advantageously enablesthe drone 202 of FIG. 2 to make, and/or to determine the need for, suchadjustments in real time during a flight of the drone 202 without thedrone 202 having to consume time and/or processing resources that wouldotherwise be associated with measuring, obtaining, and/or evaluatingreference signal strength data associated with the airspace 204 of FIG.2 in real time during flight.

FIG. 3 is a block diagram of an example implementation of the server 218of FIG. 2 constructed in accordance with the teachings of thisdisclosure. In the illustrated example of FIG. 3, the server 218includes an example radio transmitter 302, an example radio receiver304, an example processor 306, an example user interface 308, and anexample memory 310. However, other example implementations of the server218 may include fewer or additional structures.

The example radio transmitter 302 of FIG. 3 transmits data via one ormore radio frequency signal(s) to other devices (e.g., the drone 202 ofFIG. 2, other drones, etc.). In some examples, the data and/or signal(s)transmitted by the radio transmitter 302 is/are communicated over acellular network via one or more cellular base station(s) (e.g., thecellular base station(s) 206, 208, 210, 212, 214 of FIG. 2). In otherexamples, the data and/or signal(s) transmitted by the radio transmitter302 may alternatively be communicated over a local wireless area networkvia one or more wireless access point(s) operating in accordance withone or more wireless communication protocol(s) such as Wi-Fi.

In some examples, the data and/or signal(s) transmitted by the radiotransmitter 302 of FIG. 3 may include control data for a drone (e.g.,data associated with controlling the flight operations and/or the routeof the drone). In some examples, the data and/or signal(s) transmittedby the radio transmitter 302 may include and/or correspond to one ormore instruction(s), command(s), and/or request(s) for a drone to travelto, and/or to collect reference signal strength data from, one or moresampling location(s) within an airspace. In some examples, the dataand/or signal(s) transmitted by the radio transmitter 302 of FIG. 3 mayinclude a model for a drone to identify favored wireless service areas.In some examples, the model may include favored wireless service areasassociated with an airspace within which the drone is traveling and/orwithin which the drone is to travel. In the example of FIG. 3, thetransmitter 302 is a means to transmit a model to a drone. Datacorresponding to the signal(s) to be transmitted by the radiotransmitter 302 may be of any type, form and/or format, and may bestored in a non-transitory computer-readable storage medium such as theexample memory 310 of FIG. 3 described below.

The example radio receiver 304 of FIG. 3 collects, acquires and/orreceives data and/or one or more radio frequency signal(s) from otherdevices (e.g., the drone 202 of FIG. 2, other drones, etc.). In someexamples, the data and/or signal(s) received by the radio receiver 304is/are communicated over a cellular network via one or more cellularbase station(s) (e.g., the cellular base station(s) 206, 208, 210, 212,214 of FIG. 2). In other examples, the data and/or signal(s) received bythe radio receiver 304 may alternatively be communicated over a localwireless area network via one or more wireless access point(s).

In some examples, the data and/or signal(s) received by the radioreceiver 304 of FIG. 3 may include and/or correspond to reference signalstrength data collected by one or more drone(s). In some examples, thereference signal strength data may be associated with a location of adrone within an airspace. In some examples, the reference signalstrength data may be associated with one or more cell(s) and/or wirelessservice area(s) located within the airspace. Data carried by, identifiedand/or derived from the signal(s) collected and/or received by the radioreceiver 304 may be of any type, form and/or format, and may be storedin a non-transitory computer-readable storage medium such as the examplememory 310 of FIG. 3 described below.

The example processor 306 of FIG. 3 may be implemented by asemiconductor device such as a microprocessor or microcontroller. Theprocessor 306 manages and/or controls the operation of the server 218.The example processor 306 of FIG. 3 includes an example database builder312 and an example model developer 314. In some examples, the processor306 manages and/or controls the operation of the processor 306 based ondata, information and/or one or more signal(s) obtained and/or accessedby the processor 306 from one or more of the radio receiver 304, theuser interface 308, the memory 310, the database builder 312, and/or themodel developer 314 of FIG. 3, and/or based on data, information and/orone or more signal(s) provided by the processor 306 to one or more ofthe radio transmitter 302, the user interface 308, the memory 310, thedatabase builder 312, and/or the model developer 314 of FIG. 3.

The example database builder 312 of FIG. 3 builds, constructs, and/orotherwise forms a database including data associated with favoredwireless service areas for an airspace. In the illustrated example ofFIG. 3, the database builder 312 includes an example grid builder 316,an example sampling location selector 318, an example sampler 320, andan example evaluator 322. The database built via the database builder312 is to be used as a source of input data for a model to be developedvia the model developer 314 of FIG. 3. In the example of FIG. 3, thedatabase builder 312 is a database building means to build a database offavored wireless service areas based on reference signal strength datasampled from a subset of grid locations from among a plurality of gridlocations of a grid corresponding to an airspace. Data corresponding tothe database built by the database builder 312 may be of any type, formand/or format, and may be stored in a non-transitory computer-readablestorage medium such as the example memory 310 of FIG. 3 described below.

The example grid builder 316 of FIG. 3 builds, constructs, and/orotherwise forms a three-dimensional representation of an airspace (e.g.,the three-dimensional representation 222 of the airspace 204 of FIG. 2).The grid builder 316 also builds, constructs, and/or otherwise forms agrid (e.g., the grid 224 of FIG. 2) based on the three-dimensionalrepresentation of the airspace. For example, the grid builder 316 maybuild a grid (e.g., the grid 224 of FIG. 2) by dividing athree-dimensional representation of an airspace (e.g., thethree-dimensional representation 222 of the airspace 204 of FIG. 2) intocontinuous segments bounded by fixed nodes.

The respective nodes of the grid built by the grid builder 316 of FIG. 3correspond to respective locations within the three-dimensionalrepresentation and/or, more generally, within the airspace. For example,a grid having dimensions 3×3×3 will have 64 nodes (e.g., calculated as4×4×4) corresponding to 64 locations within the three-dimensionalrepresentation of the airspace. The three-dimensional representation ofthe airspace built by the grid builder 316 may have any size, shape,and/or dimensions. The grid built by the grid builder 316 may have anysize, shape, and/or dimensions falling within the size, shape, and/ordimensions of the three-dimensional representation. In the example ofFIG. 3, the grid builder 316 is a means to build a grid based on athree-dimensional representation of an airspace. Data corresponding tothe three-dimensional representation, the grid, and/or the nodes and/orlocations of the grid built by the grid builder 316 may be of any type,form and/or format, and may be stored in a non-transitorycomputer-readable storage medium such as the example memory 310 of FIG.3 described below.

FIG. 4 is an example three-dimensional representation 400 including anexample grid 402 built by the grid builder 316 of FIG. 3. Thethree-dimensional representation 400 of FIG. 4 has dimensions of(a)×(b)×(c). The grid 402 of FIG. 4 has dimensions of 2×2×2, andaccordingly has a total of 27 (e.g., calculated as 3×3×3) nodes and/orlocations. In the illustrated example of FIG. 4, the nodes and/orlocations of the grid 402 include example sampling locations 404,example alternate locations 406, and example other locations 408. Thesampling locations 404 of the grid 402 that are visible in FIG. 4include a first example sampling location 410 having a coordinateposition of (a1, b1, c0) within the grid 402, and a second examplesampling location 412 having a coordinate position of (a0, b0, c2)within the grid. The alternate locations 406 of the grid 402 that arevisible in FIG. 4 include a first example alternate location 414 havinga coordinate position of (a1, b2, c1) within the grid 402, and a secondexample alternate location 416 having a coordinate position of (a2, b2,c2) within the grid. The other locations 408 of the grid 402 include thelocations of the grid 402 other than the sampling locations 404 and thealternate locations 406 of the grid 402. The sampling locations 404,alternate locations 406, and other locations 408 of the grid 402 of FIG.4 are further described below.

The example sampling location selector 318 of FIG. 3 selects and/oridentifies sampling locations from among the nodes and/or locations ofthe grid built by the grid builder 316 of FIG. 3. For example, in theillustrated example of FIG. 4, the sampling location selector 318 hasselected the first sampling location 410 and the second samplinglocation 412 as sampling locations for the grid 402. In some examples,the sampling location selector 318 may select (e.g., randomly orpseudo-randomly select) the sampling locations of the grid as apredetermined and/or threshold number of nodes and/or locations (e.g., 5locations, 10 locations, 100 locations, etc.) from among the totalnumber of nodes and/or locations of the grid. In other examples, thesampling location selector 318 may select (e.g., randomly orpseudo-randomly select) the sampling locations of the grid as apredetermined and/or threshold percentage (e.g., 1%, 5%, 10%, etc.) ofnodes and/or locations from among the total number of nodes and/orlocations of the grid. In the example of FIG. 3, the sampling locationselector 318 is a means to select a subset of grid locations from amonga plurality of grid locations. Data corresponding to the samplinglocations selected by the sampling location selector 318 may be of anytype, form and/or format, and may be stored in a non-transitorycomputer-readable storage medium such as the example memory 310 of FIG.3 described below.

The example sampler 320 of FIG. 3 instructs, controls, and/or commandsone or more drone(s) (e.g., the drone 202 of FIG. 2, or other drones) totravel to and collect reference signal strength data from samplinglocations within the grid corresponding to the sampling locationsselected and/or identified by the sampling location selector 318 of FIG.3. The sampler 320 of FIG. 3 further instructs, controls, and/orcommands the one or more drone(s) to transmit the collected referencesignal strength data to the evaluator 322 and/or, more generally, to theserver 218 of FIG. 3. For example, in connection with the illustratedexample of FIG. 4, the sampler 320 may instruct one or more drone(s) totravel to and collect reference signal strength data from the firstsampling location 410 and the second sampling location 412 within thegrid 402, and may further instruct the drone(s) to transmit thecollected reference signal strength data to the evaluator 322 and/or theserver 218 of FIG. 3. Instructions and/or commands issued via thesampler 320 of FIG. 3 may be transmitted to the drone(s) via the radiotransmitter 302 of FIG. 3. In some examples, the instructions and/orcommands issued via the sampler 320 may take the form of requests forinformation and/or data from the drone(s). In the example of FIG. 3, thesampler 320 is a means to instruct one or more drones to collectreference signal strength data from a subset of grid locations. Datacorresponding to the instructions and/or commands issued by the sampler320 may be of any type, form and/or format, and may be stored in anon-transitory computer-readable storage medium such as the examplememory 310 of FIG. 3 described below.

The reference signal strength data to be collected by the drone(s) atthe sampling location(s) may be of any type and/or form. For example,the reference signal strength data may include reference signal receivepower (RSRP) data, reference signal strength indicator (RSSI) data,and/or reference signal receive quality (RSRQ) data. The referencesignal strength data to be collected by the drone(s) at each samplinglocation is to be associated with one or more cell(s) of one or morecellular base station(s) located within the grid of thethree-dimensional representation of the airspace (e.g., the cell(s) ofthe cellular base station(s) 206, 208, 210, 212, 214 located within thegrid 224 of the three-dimensional representation 222 of the airspace 204of FIG. 2), and/or one or more area(s) of one or more wireless accesspoint(s).

In some examples, the reference signal strength data received at theevaluator 322 and/or the server 218 of FIG. 3 from the drone(s) mayinclude and/or be associated with sampling location data indicating thesampling location from which the reference signal strength data wascollected, cell identifier data indicating one or more cell(s) fromwhich the reference signal strength data was collected, cellular basestation identifier data indicating one or more cellular base station(s)from which the reference signal strength data was collected, areaidentifier data indicating one or more area(s) from which the referencesignal strength data was collected, and/or wireless access pointidentifier data indicating one or more of wireless access point(s) fromwhich the reference signal strength data was collected. Datacorresponding to the reference signal strength data received at theevaluator 322 and/or the server 218 of FIG. 3 from the drone(s) may beof any type, form and/or format, and may be stored in a non-transitorycomputer-readable storage medium such as the example memory 310 of FIG.3 described below.

The example evaluator 322 of FIG. 3 evaluates reference signal strengthdata received at the server 218 of FIG. 3 (e.g., reference signalstrength data transmitted from the drone(s) to the server 218 inresponse to the instructions, commands, and/or requests issued via thesampler 320 of FIG. 3) to determine one or more favored wireless servicearea(s) for respective ones of the sampling locations within the grid.For example, in connection with the illustrated example of FIG. 4, theevaluator 322 may evaluate reference signal strength data received atthe server 218 of FIG. 3 to determine one or more favored wirelessservice area(s) for respective ones of the first sampling location 410and the second sampling location 412 of the grid 402. In some examples,the evaluator 322 may select the wireless service area having thegreatest reference signal strength associated with a sampling locationto be the favored wireless service area for the sampling location. Inother examples, the evaluator 322 may select all wireless service areasassociated with a sampling location and having associated referencesignal strengths that exceed a signal strength threshold to be thefavored wireless service areas for the sampling location. In the exampleof FIG. 3, the evaluator 322 is a means to determine favored wirelessservice areas for a subset of grid locations based on reference signalstrength data. Data corresponding to the favored wireless servicearea(s) determined by the evaluator 322 for respective ones of thesampling locations within the grid may be of any type, form and/orformat, and may be stored in a non-transitory computer-readable storagemedium such as the example memory 310 of FIG. 3 described below.

In some examples, the evaluator 322 of FIG. 3 may also evaluateadditional reference signal strength data received at the server 218 ofFIG. 3 to determine one or more favored wireless service area(s) forrespective ones of alternate locations within the grid. For example, theevaluator 322 and/or, more generally, the server 218 of FIG. 3 mayreceive additional reference signal strength data associated with one ormore example alternate location(s) within the grid (e.g., locationsother than the sampling locations) from one or more drone(s) that maycollect and/or sample such additional reference signal strength datawithout having been instructed, controlled, and/or commanded to do so bythe sampler 320 of FIG. 3. In connection with the illustrated example ofFIG. 4, the evaluator 322 may evaluate reference signal strength datareceived at the server 218 of FIG. 3 to determine one or more favoredwireless service area(s) for respective ones of the first alternatelocation 414 and the second alternate location 416 of the grid 402.

The additional reference signal strength data collected by the drone(s)at the alternate location(s) may be of any type and/or form. Forexample, the reference signal strength data may include RSRP data, RSSIdata, and/or RSRQ data. The reference signal strength data collected bythe drone(s) at each alternate location is to be associated with one ormore cell(s) of one or more cellular base station(s) located within thegrid of the three-dimensional representation of the airspace (e.g., thecell(s) of the cellular base station(s) 206, 208, 210, 212, 214 locatedwithin the grid 224 of the three-dimensional representation 222 of theairspace 204 of FIG. 2), and/or one or more area(s) of one or morewireless access point(s).

In some examples, the additional reference signal strength data receivedat the evaluator 322 and/or the server 218 of FIG. 3 from the drone(s)may include and/or be associated with alternate location data indicatingthe alternate location within the grid from which the reference signalstrength data was collected, cell identifier data indicating one or morecell(s) from which the reference signal strength data was collected,cellular base station identifier data indicating one or more of cellularbase station(s) from which the reference signal strength data wascollected, area identifier data indicating one or more area(s) fromwhich the reference signal strength data was collected, and/or wirelessaccess point identifier data indicating one or more of wireless accesspoint(s) from which the reference signal strength data was collected.Data corresponding to the additional reference signal strength datareceived at the evaluator 322 and/or the server 218 of FIG. 3 from thedrone(s) may be of any type, form and/or format, and may be stored in anon-transitory computer-readable storage medium such as the examplememory 310 of FIG. 3 described below.

In some examples, the evaluator 322 may select the wireless service areahaving the greatest reference signal strength associated with analternate location to be the favored wireless service area for thealternate location. In other examples, the evaluator 322 may select allwireless service areas associated with an alternate location and havingassociated reference signal strengths that exceed a signal strengththreshold to be the favored wireless service areas for the alternatelocation. Data corresponding to the favored wireless service area(s)determined by the evaluator 322 for respective ones of the alternatelocations within the grid may be of any type, form and/or format, andmay be stored in a non-transitory computer-readable storage medium suchas the example memory 310 of FIG. 3 described below.

The database builder 312 of FIG. 3 builds and maintains a database offavored wireless service areas for the sampling locations and/or thealternate locations of the grid of the three-dimensional representationof the airspace. In some examples, the database builder 312 builds andmaintains the database of favored wireless service areas for thesampling locations and/or the alternate locations of the grid based onthe above-described operations performed by, and/or the above-describeddata generated by, respective ones of the grid builder 316, the samplinglocation selector 318, the sampler 320, and/or the evaluator 322 of FIG.3.

In some examples, the database built by the database builder 312 of FIG.3 may be updated on an ongoing basis (e.g., a periodic basis, acontinuous basis, etc.). In some examples, the database may be updatedbased on, or in response to, the evaluator 322 and/or the server 218 ofFIG. 3 receiving updated (e.g., new) reference signal strength dataassociated with sampling locations and/or alternate locations of thegrid of the grid. In some examples, the evaluator 322 of FIG. 3 mayreevaluate previously received reference signal strength data in view of(e.g., together with) updated and/or more recently received referencesignal strength data to redetermine the favored wireless service area(s)based on the updated data.

The example model developer 314 of FIG. 3 develops a model to predictfavored wireless service areas for an airspace based on the databasebuilt by the database builder 312 of FIG. 3. For example, the modeldeveloper 314 may develop a model to predict favored wireless serviceareas for other locations of a grid (e.g., other locations of theexample grid 224 of FIG. 2, the example other locations 408 of theexample grid 402 of FIG. 4, etc.) based on the favored wireless serviceareas determined by the database builder 312 for the sampling locationsand/or the alternate locations of the grid (e.g., the example samplinglocations 226 and/or the example alternate locations 228 of the examplegrid 224 of FIG. 2, the example sampling locations 404 and/or theexample alternate locations 406 of the example grid 402 of FIG. 4,etc.). In the illustrated example of FIG. 3, the model developer 314 ofFIG. 3 includes an example correlation grid selector 324 and an examplecorrelation grid evaluator 326. In the example of FIG. 3, the modeldeveloper 314 is a means to develop a model to predict favored wirelessservice areas for a plurality of grid locations based on a database.Data corresponding to the model developed by the model developer 314 maybe of any type, form and/or format, and may be stored in anon-transitory computer-readable storage medium such as the examplememory 310 of FIG. 3 described below.

As used herein, the term “correlation grid” refers to any continuoussub-grid of a grid. For example, a sub-grid of a grid having M×N×Qcontinuous locations may be described as a correlation grid The examplecorrelation grid selector 324 of FIG. 3 selects and/or identifies acorrelation grid of a grid built by the grid builder 316 of FIG. 3. Forexample, the correlation grid selector 324 may select (e.g., randomly orpseudo-randomly select) a correlation grid of the example grid 224 ofFIG. 2, or the example grid 402 of FIG. 4. In some examples, thecorrelation grid selected by the correlation grid selector 324 mayinclude one or more test location(s) to be used to train the model. Thetest locations may be sampling locations and/or alternate locations froma correlation grid that neighbors and/or overlaps the correlation gridselected by the correlation grid selector 324. In the example of FIG. 3,the correlation grid selector 324 is a means to select a correlationgrid from a grid. Data corresponding to the correlation grid selected bythe correlation grid selector 324 may be of any type, form and/orformat, and may be stored in a non-transitory computer-readable storagemedium such as the example memory 310 of FIG. 3 described below.

FIG. 5 illustrates an example correlation grid 500 having example testlocations 502 for training a model. In the illustrated example of FIG.5, the test locations 502 include a first example test location 504having a coordinate position of (a0, b1, c0) within the correlation grid500, a second example test location 506 having a coordinate position of(a0, b2, c0) within the correlation grid 500, and a third example testlocation 508 having a coordinate position of (a0, b2, c2) within thecorrelation grid 500. The correlation grid 500 also includes the examplesampling locations 404 and the example alternate locations 406 of theexample grid 402 of FIG. 4 described above. The correlation grid 500 ofFIG. 5 may be selected by the correlation grid selector 324 of FIG. 3.

The example correlation grid evaluator 326 of FIG. 3 develops a jointdistribution with adjustable parameters to model the relationshipbetween the grid locations (e.g., all of the grid locations) of thecorrelation grid selected by the correlation grid selector 324 of FIG.3. In some examples, the correlation grid evaluator 326 may implementand/or execute a conditional random field (CRF) process that may beexpressed and/or defined as follows:

$\begin{matrix}{{P( x_{V} )} = {\frac{1}{Z}\underset{v \in V}{\Pi}{\varphi_{v}( x_{v} )}\underset{{({v,v^{\prime}})} \in E}{\Pi}{\psi_{{vv}^{\prime}}( {x_{v,}x_{v^{\prime}}} )}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In Equation 1, x_(V)=(x₁, x₂, . . . , x_(MNQ)) represents all the MNQlocations favored wireless service areas. For example, x_1 may be thecell and/or area identifier of a favored wireless service area for afirst location. ϕ_(v)(v) is the node potential function for node v.ψ_(vv′)(x_(v),x_(v′)) is the edge potential of (v, v′). Both ϕ and ψ arenon-negative functions parameterized by a parameter set θ. Z is anormalized constant to ensure that the whole distribution sums up to 1over its possible values.

In some examples, the functions ϕ and ψ of Equation 1 may be expressedand/or defined as follows:

$\begin{matrix}{{\varphi_{v}( x_{v} )} = {\exp ( {\sum\limits_{k = 0}^{K}\; {w_{k}{\sum\limits_{{s \in S},{{d{({s,v})}} = k}}{1( {x_{v} = x_{s}^{*}} )}}}} )}} & {{Equation}\mspace{14mu} 2} \\{{\psi_{{vv}^{\prime}}( {x_{v},x_{v^{\prime}}} )} = {\exp ( {m_{{vv}^{\prime}}1( {x_{v} \neq x_{v^{\prime}}} )} )}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In Equations 2 and 3, x_(v) is a random variable representing theoptimum cell and/or area identifier of grid location v. K is the maximumdistance in hops between two locations in the correlation grid. w_(k)and m_(vv′) are the model parameters (e.g., θ). w_(k) is the trainedparameters for locations which are k-hops away. S is the set of samplinglocations. x_(s)* is the optimal cell and/or area identifier of thesampling point s. d(s,v) is the distance in hops between s and v in thegrid.

The parameter set θ is to be determined based on the sampling locations.Once the parameter set θ has been determined, a maximum likelihoodprocess may be applied to predict favored wireless service areas for anylocation of the correlation grid. In some examples, a maximum likelihoodprocess associated with Equation 1 may be expressed and/or defined as:

$\begin{matrix}{{P( x_{V}^{*} )} = {\max\limits_{x_{V}}{\frac{1}{Z}\underset{v \in V}{\Pi}{\varphi_{v}( x_{v} )}\underset{{({v,v^{\prime}})} \in E}{\Pi}{\psi_{{vv}^{\prime}}( {x_{v,}x_{v^{\prime}}} )}}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

In the example of FIG. 3, the correlation grid developer 326 is a meansto predict favored wireless service areas for correlation gridlocations. Data corresponding to the favored wireless service areasdetermined by the correlation grid evaluator 326 for the respectivelocations of the correlation grid selected by the correlation gridselector 324 may be of any type, form and/or format, and may be storedin a non-transitory computer-readable storage medium such as the examplememory 310 of FIG. 3 described below.

In some examples, the correlation grid selector 324, the correlationgrid evaluator 326, and/or, more generally, the model developer 314 ofFIG. 3 may update, train, and/or refine the model by continuing toselect and evaluate different correlation grids of the grid in the samemanner described above. In some examples, the model developer 314 ofFIG. 3 may receive one or more input(s), instruction(s), and/orcommand(s) via the user interface 308 of FIG. 3 providing an indicationas to whether the model developer 314 is to continue updating, training,and/or refining the model.

The model developed by the model developer 314 of FIG. 3 may betransmitted to a drone. For example, the model developed by the modeldeveloper 314 may be transmitted via the radio transmitter 302 of theserver 218 of FIGS. 2 and/or 3 to the drone 202 of FIG. 2. In someexamples, the model may be transmitted to the drone prior to the dronetraveling into the airspace with which the model is associated (e.g.,the airspace 204 of FIG. 2). In other examples, the model may betransmitted to the drone while the drone is traveling within theairspace with which the model is associated (e.g., the airspace 204 ofFIG. 2). In some examples, the model may be transmitted to the drone inresponse to a request for the model received at the example server 218of FIGS. 2 and/or 3 from the drone.

The example user interface 308 of FIG. 3 facilitates interactions and/orcommunications between an end user and the server 218. The userinterface 308 includes one or more input device(s) 328 via which theuser may input information and/or data to the server 218. For example,the user interface 308 may be a button, a switch, a microphone, and/or atouchscreen that enable(s) the user to convey data and/or commands tothe example processor 306 of FIG. 3 described above, and/or, moregenerally, to the server 218 of FIGS. 2 and/or 3. The user interface 308of FIG. 3 also includes one or more output device(s) 330 via which theuser interface 308 presents information and/or data in visual and/oraudible form to the user. For example, the user interface 308 mayinclude a light emitting diode, a touchscreen, and/or a liquid crystaldisplay for presenting visual information, and/or a speaker forpresenting audible information. Data and/or information that ispresented and/or received via the user interface 308 may be of any type,form and/or format, and may be stored in a non-transitorycomputer-readable storage medium such as the example memory 310 of FIG.3 described below.

The example memory 310 of FIG. 3 may be implemented by any type(s)and/or any number(s) of storage device(s) such as a storage drive, aflash memory, a read-only memory (ROM), a random-access memory (RAM), acache and/or any other physical storage medium in which information isstored for any duration (e.g., for extended time periods, permanently,brief instances, for temporarily buffering, and/or for caching of theinformation). The information stored in the memory 310 may be stored inany file and/or data structure format, organization scheme, and/orarrangement.

The memory 310 is accessible to one or more of the example radiotransmitter 302, the example radio receiver 304, the example processor306 (e.g., including one or more of the example database builder 312,the example model developer 314, the example grid builder 316, theexample sampling location selector 318, the example sampler 320, theexample evaluator 322, the example correlation grid selector 324, and/orthe example correlation grid evaluator 326), the example user interface308, and/or, more generally, the server 218 of FIGS. 2 and/or 3.

While an example manner of implementing the server 218 of FIG. 2 isillustrated in FIG. 3, one or more of the elements, processes and/ordevices illustrated in FIG. 3 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample radio transmitter 302, the example radio receiver 304, theexample processor 306, the example user interface 308, the examplememory 310, the example database builder 312, the example modeldeveloper 314, the example grid builder 316, the example samplinglocation selector 318, the example sampler 320, the example evaluator322, the example correlation grid selector 324, and/or the examplecorrelation grid evaluator 326 of FIG. 3 may be implemented by hardware,software, firmware and/or any combination of hardware, software and/orfirmware. Thus, for example, any of the example radio transmitter 302,the example radio receiver 304, the example processor 306, the exampleuser interface 308, the example memory 310, the example database builder312, the example model developer 314, the example grid builder 316, theexample sampling location selector 318, the example sampler 320, theexample evaluator 322, the example correlation grid selector 324, and/orthe example correlation grid evaluator 326 of FIG. 3 could beimplemented by one or more analog or digital circuit(s), logic circuits,programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example radiotransmitter 302, the example radio receiver 304, the example processor306, the example user interface 308, the example memory 310, the exampledatabase builder 312, the example model developer 314, the example gridbuilder 316, the example sampling location selector 318, the examplesampler 320, the example evaluator 322, the example correlation gridselector 324, and/or the example correlation grid evaluator 326 of FIG.3 is/are hereby expressly defined to include a non-transitory computerreadable storage device or storage disk such as a memory, a digitalversatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc.including the software and/or firmware. Further still, the exampleserver 218 of FIGS. 2 and/or 3 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 3, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

FIG. 6 is a block diagram of an example implementation of the drone 202of FIG. 2 constructed in accordance with the teachings of thisdisclosure. In the illustrated example of FIG. 6, the drone 202 includesan example GPS receiver 602, an example radio transmitter 604, anexample radio receiver 606, one or more example antenna(s) 608, anexample controller 610, an example user interface 612, and an examplememory 614. However, other example implementations of the drone 202 mayinclude fewer or additional structures.

The example GPS receiver 602 of FIG. 6 collects, acquires and/orreceives data and/or one or more signal(s) from one or more GPSsatellite(s) (e.g., represented by the GPS satellites 230 of FIG. 2).Typically, signals from three or more satellites are needed to form theGPS triangulation. The data and/or signal(s) received by the GPSreceiver 602 may include information (e.g., time stamps) from which thecurrent position and/or location of the drone 202 may be identifiedand/or derived, including for example, the current latitude, longitudeand altitude of the drone 202. Location data identified and/or derivedfrom the signal(s) collected and/or received by the GPS receiver 602 maybe associated with one or more local time(s) (e.g., time stamped) atwhich the data and/or signal(s) were collected and/or received by theGPS receiver 602. In some examples, a local clock is used to timestampthe location data. Location data identified and/or derived from thesignal(s) collected and/or received by the GPS receiver 602 may be ofany type, form and/or format, and may be stored in a non-transitorycomputer-readable storage medium such as the example memory 614 of FIG.6 described below.

The example radio transmitter 604 of FIG. 6 transmits data via one ormore radio frequency signal(s) to other devices (e.g., the server 218 ofFIGS. 2 and/or 3, etc.). In some examples, the data and/or signal(s)transmitted by the radio transmitter 604 is/are communicated over acellular network via one or more cellular base station(s) (e.g., thecellular base station(s) 206, 208, 210, 212, 214 of FIG. 2). In otherexamples, the data and/or signal(s) transmitted by the radio transmitter604 may alternatively be communicated over a local wireless area networkvia one or more wireless access point(s) operating in accordance withone or more wireless communication protocol(s) such as Wi-Fi.

In some examples, the data and/or signal(s) transmitted by the radiotransmitter 604 of FIG. 6 may include control data for the drone 202(e.g., data associated with controlling the flight operations and/or theroute of the drone 202). In some examples, the data and/or signal(s)transmitted by the radio transmitter 604 may include and/or correspondto reference signal strength data collected by the drone 202 from one ormore sampling location(s) within an airspace. In some examples, the dataand/or signal(s) transmitted by the radio transmitter 604 of FIG. 6 mayinclude and/or correspond to one or more request(s) for a model topredict favored wireless service areas associated with an airspacewithin which the drone 202 is traveling and/or within which the drone202 is to travel. Data corresponding to the signal(s) to be transmittedby the radio transmitter 604 may be of any type, form and/or format, andmay be stored in a non-transitory computer-readable storage medium suchas the example memory 614 of FIG. 6 described below.

The example radio receiver 606 of FIG. 6 collects, acquires and/orreceives data and/or one or more radio frequency signal(s) from otherdevices (e.g., the server 218 of FIGS. 2 and/or 3, etc.). In someexamples, the data and/or signal(s) received by the radio receiver 606is/are communicated over a cellular network via one or more cellularbase station(s) (e.g., the cellular base station(s) 206, 208, 210, 212,214 of FIG. 2). In other examples, the data and/or signal(s) received bythe radio receiver 606 may alternatively be communicated over a localwireless area network via one or more wireless access point(s) operatingin accordance with one or more wireless communication protocol(s) suchas Wi-Fi.

In some examples, the data and/or signal(s) received by the radioreceiver 606 of FIG. 6 may include control data for the drone 202 (e.g.,data associated with controlling the flight operations and/or the routeof the drone 202). In some examples, the data and/or signal(s) receivedby the radio receiver 606 of FIG. 6 may include and/or correspond to oneor more instruction(s), command(s), and/or request(s) for the drone 202to travel to, and/or to collect reference signal strength data from, oneor more sampling location(s) within an airspace. In some examples, thedata and/or signal(s) received by the radio receiver 606 of FIG. 6 mayinclude and/or correspond to a model to predict favored wireless serviceareas for the drone 202 as the drone 202 travels through an airspace.Data carried by, identified and/or derived from the signal(s) collectedand/or received by the radio receiver 606 may be of any type, formand/or format, and may be stored in a non-transitory computer-readablestorage medium such as the example memory 614 of FIG. 6 described below.

The example antenna(s) 608 of FIG. 6 provide one or more interface(s)between the radio transmitter 604 and/or the radio receiver 606 of FIG.6 and radio waves and/or radio signals propagating through, or to bepropagated through, an airspace. In some examples, the antenna(s) 608may include a first antenna associated with the radio transmitter 604 ofFIG. 6 and a second antenna associated with the radio receiver 606 ofFIG. 6. In other examples, the antenna(s) 608 may include a singleantenna associated with both the radio transmitter 604 and the radioreceiver 606 of FIG. 6. In some examples, the antenna 608 or, in thecase of multiple antennas, respective ones of the antennas 608 of FIG. 6may be implemented as a directional antenna having an associateddirectional communication beam that that may be steered and/orpositioned (e.g., either by changing the direction and/or position ofthe antenna 608 relative to the direction and/or position of the drone202, or by changing the direction and/or position of the drone 202itself) in a desired direction. In other examples, the antenna 608 or,in the case of multiple antennas, respective ones of the antenna(s) 608of FIG. 6 may be implemented as an omnidirectional antenna having anassociated omnidirectional communication beam that radiates uniformly inall directions within a plane of an airspace.

The example controller 610 of FIG. 6 may be implemented by asemiconductor device such as a microprocessor or microcontroller. Thecontroller 610 manages and/or controls the operation of the drone 202.In the illustrated example of FIG. 6, the example controller 610includes an example prediction engine 616, an example route manager 618,and an example calibrator 620. In some examples, the controller 610manages and/or controls the operation of the drone 202 based on data,information and/or one or more signal(s) obtained and/or accessed by thecontroller 610 from one or more of the GPS receiver 602, the radioreceiver 606, the antenna(s) 608, the user interface 612, the memory614, the prediction engine 616, the route manager 618, and/or thecalibrator 620 of FIG. 6, and/or based on data, information and/or oneor more signal(s) provided by the controller 610 to one or more of theradio transmitter 604, the antenna(s) 608, the user interface 612, theprediction engine 616, the route manager 618, and/or the calibrator 620of FIG. 6.

The example prediction engine 616 of FIG. 6 implements and/or executesthe model developed by the model developer 314 of the server 218 ofFIGS. 2 and/or 3. In some examples, the prediction engine 616 makesand/or issues one or more request(s) for the server 218 to transmit themodel to the drone 202 of FIGS. 2 and/or 6. In some examples, theprediction engine 616 implements and/or executes the model to identifyand/or select favored wireless service areas in real time during flightof the drone 202 based on the location of the drone 202, and without thedrone 202 having to consume time and/or processing resources that wouldotherwise be associated with measuring, obtaining, and/or evaluatingreference signal strength data associated with the airspace. The exampleprediction engine 616 of FIG. 6 includes an example location identifier622, an example correlation grid identifier 624, an example service areaidentifier 626, and an example service area selector 628.

FIG. 7 illustrates an example environment of use 700 in which theexample prediction engine 616 of the example drone 202 of FIGS. 2 and/or6 may implement a model developed by the example model developer 314 ofthe example server 218 of FIGS. 2 and/or 3 to identify and selectfavored wireless service areas for an example route 702 of the drone 202in real time during flight, and without the drone 202 having to consumetime and/or processing resources that would otherwise be associated withmeasuring, obtaining, and/or evaluating reference signal strength dataassociated with the airspace. The environment of use 700 of FIG. 7includes the airspace 204 of FIG. 2. In the illustrated example of FIG.7, the drone 202 is shown traveling within the airspace 204 along theroute 702 at three different instances in time and/or location relativeto the route 702. More specifically, the drone 202 is shown in FIG. 7 ata first example current location 704, a second example current location706 subsequent in time and/or location to the first current location704, and a third example current location 708 subsequent in time and/orlocation to the second current location 706. Respective grid locations(e.g., a first example grid location 710, a second example grid location712, and a third example grid location 714) associated withcorresponding respective ones of the current locations of the drone 202are also shown in FIG. 7. Respective correlation grids (e.g., a firstexample correlation grid 716, a second example correlation grid 718, anda third example correlation grid location 720) associated withcorresponding respective ones of the grid locations are also shown inFIG. 7. Respective favored wireless service areas (e.g., first examplefavored wireless service areas 722, second example favored wirelessservice areas 724, and third example favored wireless service areas 726)associated with corresponding respective ones of the correlation gridsare also shown in FIG. 7. The current locations of the drone, the gridlocations, the correlation grids, and the favored wireless service areasof FIG. 7 are further described below.

The example location identifier 622 of FIG. 6 identifies a currentlocation of the drone 202 of FIGS. 2 and/or 6. For example, the locationidentifier 622 may determine a current location of the drone 202 basedon the most current location data identified and/or derived from thesignal(s) collected and/or received by the GPS receiver 602 of FIG. 6.In the illustrated example of FIG. 7, the location identifier 622 hasidentified the first current location 704 of the drone 202 at a firstinstance in time, the second current location 706 of the drone 202 at asecond instance in time following the first instance, and the thirdcurrent location 708 of the drone 202 at a third instance in timefollowing the second instance. In the examples of FIGS. 6 and 7, thelocation identifier 622 is a means to identify a location of the droneduring a flight of the drone. Data corresponding to the current locationof the drone 202 identified by the location identifier 622 of FIG. 6 maybe of any type, form and/or format, and may be stored in anon-transitory computer-readable storage medium such as the examplememory 614 of FIG. 6 described below.

The example correlation grid identifier 624 of FIG. 6 identifies acorrelation grid of the model based on the current location of the drone202 of FIGS. 2 and/or 6. For example, the correlation grid identifier624 of FIG. 6 may compare the current location of the drone to each gridlocation of the model to determine and/or identify a grid location thatis most proximate to the current location of the drone. Based on thedetermination and/or identification of the grid location, thecorrelation gird identifier 624 may identify a correlation grid of themodel. For example, the correlation grid identifier 624 may identify acorrelation grid having a central node and/or location that is mostproximate to the grid location. In the illustrated example of FIG. 7,the correlation grid identifier 624 has identified the first gridlocation 710 based on the first current location 704 of the drone 202,the second grid location 712 based on the second current location 706 ofthe drone 202, and the third grid location 714 based on the thirdcurrent location 708 of the drone 202. The correlation grid identifier624 has also identified the first correlation grid 716 based on thefirst grid location 710, the second correlation grid 718 based on thesecond grid location 712, and the third correlation grid 720 based onthe third grid location 714. In the examples of FIGS. 6 and 7, thecorrelation grid identifier 624 is a means to identify a correlationgrid based on the location of the drone. Data corresponding to the gridlocation and/or the correlation grid identified by the correlation grididentifier 624 of FIG. 6 may be of any type, form and/or format, and maybe stored in a non-transitory computer-readable storage medium such asthe example memory 614 of FIG. 6 described below.

The example service area identifier 626 of FIG. 6 identifies one or morefavored wireless service area(s) associated with the correlation grididentified by the correlation grid identifier 624 of FIG. 6. Forexample, the service area identifier 626 may identify the favoredwireless service area(s) that the model has associated with and/oridentified for the specific correlation grid that has been identified bythe correlation grid identifier 624. In the illustrated example of FIG.7, the service area identifier 626 has identified the first favoredwireless service areas 722 associated with the first correlation grid716, the second favored wireless service areas 724 associated with thesecond correlation grid 718, and the third favored wireless serviceareas 726 associated with the third correlation grid 720. In theexamples of FIGS. 6 and 7, the service area identifier 626 is a means toidentify favored wireless service areas during a flight of the drone.Data corresponding to the favored wireless service area(s) identified bythe service area identifier 626 of FIG. 6 may be of any type, formand/or format, and may be stored in a non-transitory computer-readablestorage medium such as the example memory 614 of FIG. 6 described below.

The example service area selector 628 of FIG. 6 selects one of the oneor more favored wireless service area(s) identified by the service areaidentifier 626 of FIG. 6 and associated with the correlation grididentified by the correlation grid identifier 624 of FIG. 6. Forexample, the service area selector 628 may select the favored wirelessservice area that is most proximate to the current location of the drone202 of FIGS. 2 and/or 6. As another example, the service area selector628 may select the favored wireless service area that is most proximateto a route of travel of the drone 202, and/or most proximate adestination location associated with the route of travel. In theillustrated example of FIG. 7, the service area selector 628 may selecta first example favored wireless service area 728 from among the firstfavored wireless service areas 722, a second example favored wirelessservice area 730 from among the second favored wireless service areas724, and a third example favored wireless service area 732 from amongthe third favored wireless service areas 726. In the examples of FIGS. 6and 7, the service area selector 628 is a means to select an identifiedone of the favored wireless service areas during a flight of the drone.Data corresponding to the favored wireless service area selected by theservice area selector 628 of FIG. 6 may be of any type, form and/orformat, and may be stored in a non-transitory computer-readable storagemedium such as the example memory 614 of FIG. 6 described below.

The example route manager 618 of FIG. 6 generates, manages, and/orcontrols a route of the drone 202 of FIGS. 2 and/or 6 based on data,information and/or one or more signal(s) obtained and/or accessed by theroute manager 618 from one or more of the GPS receiver 602, the radioreceiver 606, the antenna(s) 608, the controller 610, the user interface612, the memory 614, and/or the prediction engine 616 of FIG. 6, and/orbased on data, information and/or one or more signal(s) provided by theroute manager 618 to one or more of the radio transmitter 604, theantenna(s) 608, the controller 610, the user interface 612, the memory614, and/or the prediction engine 616 of FIG. 6. In some examples, theroute manager 618 generates, manages, and/or controls the route of thedrone 202 based on control data received at the drone 202 from theserver 218 of FIGS. 2 and/or 3. In some examples, the route manager 618generates, manages, and/or controls the route of the drone 202 based onthe model received at the drone 202 from the server 218 of FIGS. 2and/or 3.

In some examples, the route manager 618 of FIG. 6 adjusts, changes,and/or alters a position of the drone and/or a route to be followedduring a flight of the drone 202 of FIGS. 2 and/or 6 based on thefavored wireless service area selected by the service area selector 628of the prediction engine 616 of FIG. 6. For example, the route manager618 may instruct and/or command the drone 202 to adjust position,course, and/or direction such that the drone 202 is more proximate tothe favored wireless service area selected by the service area selector628. As another example, the route manager 618 may instruct and/orcommand the drone 202, or a directional antenna of the drone 202, toadjust (e.g., change) position, course, and/or direction such that thedrone 202 and/or a directional communication beam associated with thedirectional antenna of the drone 202 is/are more proximate to and/orpointed toward the favored wireless service area selected by the servingcell. In the illustrated example of FIG. 7, the route manager 618 mayadjust the route 702 of the drone 202 based on the selection of thefirst favored wireless service area 728, the selection of the secondfavored wireless service area 730, and/or based on the selection of thethird favored wireless service area 732. By adjusting the positionand/or route to be followed during the flight of the drone 202 based onthe favored wireless service area selected by the service area selector628, the route manager 618 of FIG. 6 improves the likelihood that thedrone 202 will maintain a communication channel with the server 218 ofFIGS. 2 and/or 3. In the examples of FIGS. 6 and 7, the route manager618 is a means to adjust the drone during the flight of the drone basedon a selected one of the favored wireless service areas.

In some examples, the route manager 618 may continue adjusting theposition and/or route of the drone 202 in real time based on favoredwireless service areas as the drone continues to travel along the routetowards a destination location. In some examples, the route manager 618may continue adjusting the position and/or route of the drone 202 inresponse to repetition of the above-described operations of the locationidentifier 622, the correlation grid identifier 624, the service areaidentifier 626, the service area selector 628, and/or, more generally,the prediction engine 616 of FIG. 6 as the drone continues to travelalong the route. In some examples, the route manager 618 of FIG. 6 mayreceive one or more input(s), instruction(s), and/or command(s) via theuser interface 612 of FIG. 6 providing an indication as to whether theroute manager 618 is to continue adjusting the position and/or route ofthe drone based on favored wireless service areas.

The example calibrator 620 of FIG. 6 instructs, controls, and/orcommands the drone 202 of FIGS. 2 and/or 6 to collect reference signalstrength data from a calibration location within an airspace. In someexamples, the calibration location may correspond to the currentlocation of the drone 202. In other examples, the calibration locationmay correspond to a sampling point of a grid (e.g., a correlation grid)of an airspace. The calibrator 620 may compare the reference signalstrength data collected from the calibration location to referencesignal strength data collected (e.g., collected at an earlier time) froma corresponding sampling location (e.g., a sampling location mostproximate to the calibration location) to determine the accuracy of thereference signal strength data collected from the sampling location.Data corresponding to the reference signal strength data collected fromthe calibration location may be of any type, form and/or format, and maybe stored in a non-transitory computer-readable storage medium such asthe example memory 614 of FIG. 6 described below.

The example user interface 612 of FIG. 6 facilitates interactions and/orcommunications between an end user and the drone 202. The user interface612 includes one or more input device(s) 630 via which the user mayinput information and/or data to the drone 202. For example, the userinterface 612 may be a button, a switch, a microphone, and/or atouchscreen that enable(s) the user to convey data and/or commands tothe example controller 610 of FIG. 6 described above, and/or, moregenerally, to the drone 202 of FIG. 6. The user interface 612 of FIG. 6also includes one or more output device(s) 632 via which the userinterface 612 presents information and/or data in visual and/or audibleform to the user. For example, the user interface 612 may include alight emitting diode, a touchscreen, and/or a liquid crystal display forpresenting visual information, and/or a speaker for presenting audibleinformation. Data and/or information that is presented and/or receivedvia the user interface 612 may be of any type, form and/or format, andmay be stored in a non-transitory computer-readable storage medium suchas the example memory 614 of FIG. 6 described below.

The example memory 614 of FIG. 6 may be implemented by any type(s)and/or any number(s) of storage device(s) such as a storage drive, aflash memory, a read-only memory (ROM), a random-access memory (RAM), acache and/or any other physical storage medium in which information isstored for any duration (e.g., for extended time periods, permanently,brief instances, for temporarily buffering, and/or for caching of theinformation). The information stored in the memory 614 may be stored inany file and/or data structure format, organization scheme, and/orarrangement.

The memory 614 is accessible to one or more of the example GPS receiver602, the example radio transmitter 604, the example radio receiver 606,the example controller 610 (e.g., including one or more of the exampleprediction engine 616, the example route manager 618, the examplecalibrator 620, the example location identifier 622, the examplecorrelation grid identifier 624, the example service area identifier626, and/or the example service area selector 628), the example userinterface 612, and/or, more generally, the drone 202 of FIG. 6.

While an example manner of implementing the drone 202 of FIG. 2 isillustrated in FIG. 6, one or more of the elements, processes and/ordevices illustrated in FIG. 6 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample GPS receiver 602, the example radio transmitter 604, the exampleradio receiver 606, the example antenna(s) 608, the example controller610, the example user interface 612, the example memory 614, the exampleprediction engine 616, the example route manager 618, the examplecalibrator 620, the example location identifier 622, the examplecorrelation grid identifier 624, the example service area identifier626, and/or the example service area selector 628 of FIG. 6 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample GPS receiver 602, the example radio transmitter 604, the exampleradio receiver 606, the example antenna(s) 608, the example controller610, the example user interface 612, the example memory 614, the exampleprediction engine 616, the example route manager 618, the examplecalibrator 620, the example location identifier 622, the examplecorrelation grid identifier 624, the example service area identifier626, and/or the example service area selector 628 of FIG. 6 could beimplemented by one or more analog or digital circuit(s), logic circuits,programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example GPS receiver602, the example radio transmitter 604, the example radio receiver 606,the example antenna(s) 608, the example controller 610, the example userinterface 612, the example memory 614, the example prediction engine616, the example route manager 618, the example calibrator 620, theexample location identifier 622, the example correlation grid identifier624, the example service area identifier 626, and/or the example servicearea selector 628 of FIG. 6 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample drone 202 of FIGS. 2 and/or 6 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 6, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions whichmay be executed by a processor to implement the server 218 of FIGS. 2and/or 3 and the drone 202 of FIGS. 2 and/or 6 are shown in FIGS. 8-10.In these examples, the machine readable instructions are one or moreprogram(s) for execution by a processor such as the example processor306 of FIG. 3 shown in the example processor platform 1100 discussedbelow in connection with FIG. 11, and/or by the example controller 610of FIG. 6 shown in the example processor platform 1200 discussed belowin connection with FIG. 12. The program(s) may be embodied in softwarestored on a non-transitory computer readable storage medium such as aCD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), aBlu-ray disk, or a memory associated with the processor 306 of FIG. 3and/or the controller 610 of FIG. 6, but the entire program(s) and/orparts thereof could alternatively be executed by a device other than theprocessor 306 of FIG. 3 and/or the controller 610 of FIG. 6, and/orembodied in firmware or dedicated hardware. Further, although theexample program(s) is/are described with reference to the flowchartsillustrated in FIGS. 8-10, many other methods for implementing theserver 218 of FIGS. 2 and/or 3 and the drone 202 of FIGS. 2 and/or 6 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, a FieldProgrammable Gate Array (FPGA), an Application Specific IntegratedCircuit (ASIC), a comparator, an operational-amplifier (op-amp), a logiccircuit, etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example processes of FIGS. 8-10 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory (ROM), a compact disk (CD), a digital versatile disk(DVD), a cache, a random-access memory (RAM) and/or any other storagedevice or storage disk in which information is stored for any duration(e.g., for extended time periods, permanently, for brief instances, fortemporarily buffering, and/or for caching of the information). As usedherein, the term “non-transitory computer readable storage medium” isexpressly defined to include any type of computer readable storagedevice and/or storage disk and to exclude propagating signals and toexclude transmission media. “Including” and “comprising” (and all formsand tenses thereof) are used herein to be open ended terms. Thus,whenever a claim lists anything following any form of “include” or“comprise” (e.g., comprises, includes, comprising, including, etc.), itis to be understood that additional elements, terms, etc. may be presentwithout falling outside the scope of the corresponding claim. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the terms“comprising” and “including” are open ended.

FIG. 8 is a flowchart representative of example machine readableinstructions 800 that may be executed at the example server 218 of FIGS.2 and/or 3 to develop a model to predict favored wireless service areasfor a drone. The example program 800 begins when the example gridbuilder 316 of the example server 218 of FIGS. 2 and/or 3 builds athree-dimensional representation of an airspace (block 802). Forexample, the grid builder 316 may build the example three-dimensionalrepresentation 222 of FIG. 2 or the example three-dimensionalrepresentation 400 of FIG. 4. Following block 802, control proceeds toblock 804.

At block 804, the example grid builder 316 of the example server 218 ofFIGS. 2 and/or 3 builds a grid based on the three-dimensionalrepresentation (block 804). For example, the grid builder 316 may buildthe example grid 224 of FIG. 2 based on the example three-dimensionalrepresentation 222 of FIG. 2, or may build the example grid 402 based onthe example three-dimensional representation 400 of FIG. 4. Followingblock 804, control proceeds to block 806.

At block 806, the example sampling location selector 318 of the exampleserver 218 of FIGS. 2 and/or 3 selects sampling locations from the grid(block 806). For example, the sampling location selector 318 may selectthe example sampling locations 226 from the example grid 224 of FIG. 2,or may select the example sampling locations 404 from the example grid402 of FIG. 4. Following block 806, control proceeds to block 808.

At block 808, the example sampler 320 of the example server 218 of FIGS.2 and/or 3 instructs and/or commands one or more drone(s) to collectreference signal strength data from the sampling locations (block 808).For example, the sampler 320 may instruct one or more drone(s) tocollect reference signal strength data from the example samplinglocations 226 of the example grid 224 of FIG. 2, or may instruct one ormore drone(s) to collect reference signal strength data from the examplesampling locations 404 of the example grid 402 of FIG. 4. Followingblock 808, control proceeds to block 810.

At block 810, the example evaluator 322 of the example server 218 ofFIGS. 2 and/or 3 determines whether reference signal strength dataassociated with the sampling locations of the grid has been received atthe server 218 of FIGS. 2 and/or 3 (block 810). For example, theevaluator 322 may determine whether reference signal strength dataassociated with the example sampling locations 226 of the example grid224 of FIG. 2 has been received via the example radio receiver 304 ofthe server 218 of FIGS. 2 and/or 3, or may determine whether referencesignal strength data associated with the example sampling locations 404of the example grid 402 of FIG. 4 has been received via the exampleradio receiver 304 of the server 218 of FIGS. 2 and/or 3. If theevaluator 322 determines at block 810 that reference signal strengthdata associated with the sampling locations of the grid has beenreceived at the server 218, control proceeds to block 812. If theevaluator 322 instead determines at block 810 that reference signalstrength data associated with the sampling locations of the grid has notbeen received at the server 218, control remains at block 810.

At block 812, the example evaluator 322 of the example server 218 ofFIGS. 2 and/or 3 evaluates the reference signal strength data associatedwith the sampling locations of the grid to determine favored wirelessservice areas for the sampling locations (block 812). For example, theevaluator 322 may evaluate the reference signal strength data associatedwith the example sampling locations 226 of the example grid 224 of FIG.2, or may evaluate the reference signal strength data associated withthe example sampling locations 404 of the example grid 402 of FIG. 4.Following block 812, control proceeds to block 814.

At block 814, the example evaluator 322 of the example server 218 ofFIGS. 2 and/or 3 determines whether reference signal strength dataassociated with alternate locations of the grid has been received at theserver 218 of FIGS. 2 and/or 3 (block 814). For example, the evaluator322 may determine whether reference signal strength data associated withthe example alternate locations 228 of the example grid 224 of FIG. 2has been received via the example radio receiver 304 of the server 218of FIGS. 2 and/or 3, or may determine whether reference signal strengthdata associated with the example alternate locations 406 of the examplegrid 402 of FIG. 4 has been received via the example radio receiver 304of the server 218 of FIGS. 2 and/or 3. If the evaluator 322 determinesat block 814 that reference signal strength data associated withalternate locations of the grid has been received at the server 218,control proceeds to block 816. If the evaluator 322 instead determinesat block 814 that reference signal strength data associated withalternate locations of the grid has not been received at the server 218,control proceeds to block 818.

At block 816, the example evaluator 322 of the example server 218 ofFIGS. 2 and/or 3 evaluates the reference signal strength data associatedwith the alternate locations of the grid to determine favored wirelessservice areas for the alternate locations (block 816). For example, theevaluator 322 may evaluate the reference signal strength data associatedwith the example alternate locations 228 of the example grid 224 of FIG.2, or may evaluate the reference signal strength data associated withthe example alternate locations 406 of the example grid 402 of FIG. 4.Following block 816, control proceeds to block 818.

At block 818, the example model developer 314 of the example server 218of FIGS. 2 and/or 3 develops a model to predict favored wireless serviceareas for other locations of the grid based on the favored wirelessservice areas determined for the sampling locations and/or the alternatelocations (block 818). For example, the model developer 314 may developa model to predict favored wireless service areas for other locations ofthe grid based on the favored wireless service areas determined for theexample sampling locations 226 and/or the example alternate locations228 of the example grid 224 of FIG. 2, or based on the favored wirelessservice areas determined for the example sampling locations 404 and/orthe example alternate locations 406 of the example grid 402 of FIG. 2.An example process that may be used to implement block 818 of theexample program 800 of FIG. 8 is described in greater detail below inconnection with FIG. 9. Following block 818, control proceeds to block820.

At block 820, the example radio transmitter 302 of the example server218 of FIGS. 2 and/or 3 transmits the model to a drone (block 820). Forexample, the radio transmitter 302 may transmit the model to the exampledrone 202 of FIGS. 2 and/or 6. In some examples, the radio transmitter302 may transmit the model to the drone in response to a request for themodel received at the example server 218 of FIGS. 2 and/or 3 from thedrone. Following block 820, the example program 800 of FIG. 8 ends.

FIG. 9 is a flowchart representative of example machine readableinstructions 818 that may be executed at the example server 218 of FIGS.2 and/or 3 to develop a model using correlation grids to predict favoredwireless service areas for a drone. Example operations of blocks 902,904 and 906 of FIG. 9 may be used to implement block 818 of FIG. 8. Theexample program 818 begins when the example correlation grid selector324 of the example server 218 of FIGS. 2 and/or 3 selects a correlationgrid of a grid built by the example grid builder 316 of the exampleserver 218 of FIGS. 2 and/or 3. For example, the correlation gridselector 324 may select (e.g., randomly or pseudo-randomly select) acorrelation grid of the example grid 224 of FIG. 2, or a correlationgrid of the example grid 402 of FIG. 4 (block 902). Following block 902,control proceeds to block 904.

At block 904, the example correlation grid evaluator 326 of the exampleserver 218 of FIGS. 2 and/or 3 predicts favored wireless service areasfor all of the locations of the correlation grid selected by the examplecorrelation grid selector 324 of the example server 218 of FIGS. 2and/or 3 (block 904). For example, the correlation grid evaluator 326may develop a joint distribution with adjustable parameters to model therelationship between the grid locations (e.g., all of the gridlocations) of the correlation grid selected by the correlation gridselector 324 of FIG. 3. In some examples, the correlation grid evaluator326 may develop the joint distribution by implementing and/or executinga conditional random field (CRF) process that may be expressed and/ordefined according to Equations 1-4 described above. Following block 904,control proceeds to block 906.

At block 906, the example model developer 314 of FIG. 3 determineswhether to continue training the model (block 906). For example, themodel developer 314 may receive one or more input(s), instruction(s),and/or command(s) via the example user interface 308 of FIG. 3 providingan indication as to whether the model developer 314 is to continuetraining the model. If the model developer 314 determines at block 906to continue training the model, control returns to block 902. If themodel developer instead determines at block 906 not to continue trainingthe model, control of the example program 818 of FIG. 9 returns to afunction call such as block 818 of the example program 800 of FIG. 8.

FIG. 10 is a flowchart representative of example machine readableinstructions 1000 that may be executed at the example drone 202 of FIGS.2 and/or 6 to adjust the drone 202 based on favored wireless serviceareas identified and selected in real time during flight of the drone202. The example program 1000 begins when the example prediction engine616 of the example drone 202 of FIGS. 2 and/or 6 determines whether thedrone 202 has received a model to predict favored wireless service areasfor the drone 202 (block 1002). For example, the prediction engine 616may determine that the drone 202 has received, via the example radioreceiver 606 of the drone 202, a model developed by the example modeldeveloper 314 of the example server 218 of FIGS. 2 and/or 3. If theprediction engine 616 determines at block 1002 that the drone 202 hasreceived a model to predict favored wireless service areas for the drone202, control proceeds to block 1004. If the prediction engine 616instead determines at block 1002 that the drone 202 has not received amodel to predict favored wireless service areas for the drone 202,control remains at block 1002.

At block 1004, the example location identifier 622 of the example drone202 of FIGS. 2 and/or 6 identifies a current location of the drone 202(block 1004). For example, the location identifier 622 may identify thefirst example current location 704 of the drone 202 shown in FIG. 7.Following block 1004, control proceeds to block 1006.

At block 1006, the example correlation grid identifier 624 of theexample drone 202 of FIGS. 2 and/or 6 identifies a correlation gridbased on the identified current location of the drone 202 (block 1006).For example, the correlation grid identifier 624 may identify the firstexample correlation grid 716 shown in FIG. 7 based on the first examplecurrent location 704 of the drone 202 shown in FIG. 7, and further basedon the first example grid location 710 shown in FIG. 7. Following block1006, control proceeds to block 1008.

At block 1008, the example service area identifier 626 of the exampledrone 202 of FIGS. 2 and/or 6 identifies favored wireless service areasassociated with the identified correlation grid (block 1008). Forexample, the service area identifier 626 may identify the first examplefavored wireless service areas 722 of FIG. 7 associated with the firstexample correlation grid 716 shown in FIG. 7. Following block 1008,control proceeds to block 1010.

At block 1010, the example service area selector 628 of the exampledrone 202 of FIGS. 2 and/or 6 selects a favored wireless service areafrom among the identified favored wireless service areas (block 1010).For example, the service area selector 628 may identify the firstexample favored wireless service area 728 from among the first examplefavored wireless service areas 722 of FIG. 7. Following block 1010,control proceeds to block 1012.

At block 1012, the example route manager 618 of the example drone 202 ofFIGS. 2 and/or 6 adjusts the drone 202 (e.g., adjusts a position and/ora route of the drone) based on the selected favored wireless servicearea (block 1012). For example, the route manager 618 may adjust theexample route 702 of the drone 202 shown in FIG. 7 based on theselection of the first example favored wireless service area 728 of FIG.7. Following block 1012, control proceeds to block 1014.

At block 1014, the example route manager 618 of the example drone 202 ofFIGS. 2 and/or 6 determines whether to continue adjusting the dronebased on favored wireless service areas (block 1014). For example, theroute manager 618 may receive one or more input(s), instruction(s),and/or command(s) via the example user interface 612 of FIG. 6 providingan indication as to whether the route manager 618 is to continueadjusting the drone 202 based on favored wireless service areas. If theroute manager 618 determines at block 1014 to continue adjusting thedrone 202 based on favored wireless service areas, control returns toblock 1004. If the route manager 618 instead determines at block 1014not to continue adjusting the drone 202 based on favored wirelessservice areas, the example program 1000 of FIG. 10 ends.

FIG. 11 is a block diagram of an example processor platform 1100structured to execute the instructions 800 of FIGS. 8 and 9 to implementthe example server 218 of FIGS. 2 and/or 3. The processor platform 1100of the illustrated example includes a processor implemented as theexample processor 306 of FIG. 3. The processor 306 of the illustratedexample is hardware. For example, the processor 306 can be implementedby one or more integrated circuit(s), logic circuit(s),microprocessor(s), or controller(s) from any desired family ormanufacturer. The hardware processor may be a semiconductor based (e.g.,silicon based) device. The processor 306 of the illustrated exampleimplements the example database builder 312, the example model developer314, the example grid builder 316, the example sampling locationselector 318, the example sampler 320, the example evaluator 322, theexample correlation grid selector 324, and/or the example correlationgrid evaluator 326 of FIG. 3.

The processor 306 of the illustrated example includes a local memory1102 (e.g., a cache). The processor 306 of the illustrated example is incommunication with a main memory including a volatile memory 1104 and anon-volatile memory 1106 via a bus 1108. The volatile memory 1104 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 1106 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 1104, 1106 iscontrolled by a memory controller. In the illustrated example of FIG.11, one or both of the volatile memory 1104 and/or the non-volatilememory 1106 implement(s) the example memory 310 of FIG. 3.

The processor platform 1100 of the illustrated example also includes oneor more mass storage device(s) 1110 for storing software and/or data.Examples of such mass storage devices 1110 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives. In the illustratedexample of FIG. 11, the mass storage device(s) implement(s) the examplememory 310 of FIG. 3.

The processor platform 1100 of the illustrated example also includes auser interface circuit 1112. The user interface circuit 1112 may beimplemented by any type of interface standard, such as an Ethernetinterface, a universal serial bus (USB), and/or a PCI express interface.In the illustrated example, one or more input device(s) 328 areconnected to the user interface circuit 1112. The input device(s) 328permit(s) a user to enter data and commands into the processor 306. Theinput device(s) 328 can be implemented by, for example, an audio sensor,a camera (still or video), a keyboard, a button, a mouse, a touchscreen,a track-pad, a trackball, isopoint, a voice recognition system, amicrophone, and/or a liquid crystal display. One or more outputdevice(s) 330 are also connected to the user interface circuit 1112 ofthe illustrated example. The output device(s) 330 can be implemented,for example, by a light emitting diode, an organic light emitting diode,a liquid crystal display, a touchscreen and/or a speaker. The userinterface circuit 1112 of the illustrated example may, thus, include agraphics driver such as a graphics driver chip and/or processor. In theillustrated example, the input device(s) 328, the output device(s) 330,and the user interface circuit 1112 collectively implement the exampleuser interface 308 of FIG. 3.

The processor platform 1100 of the illustrated example also includes anetwork interface circuit 1114. The network interface circuit 1114 maybe implemented by any type of interface standard, such as an Ethernetinterface, a universal serial bus (USB), and/or a PCI express interface.In the illustrated example, the network interface circuit 1114 includesthe example radio transmitter 302 and the example radio receiver 304 ofFIG. 3 to facilitate the exchange of data and/or signals with externalmachines (e.g., the drone 202 of FIGS. 2 and/or 3, other drones, etc.)via a network 1116 (e.g., a cellular network, a wireless local areanetwork (WLAN), etc.).

Coded instructions 1118 including the coded instructions 800 of FIGS. 8and 9 may be stored in the local memory 1102, in the volatile memory1104, in the non-volatile memory 1106, in the mass storage device(s)1110, and/or on a removable tangible computer readable storage mediumsuch as a flash memory stick, a CD, or a DVD.

FIG. 12 is a block diagram of an example processor platform 1200structured to execute the instructions 1000 of FIG. 10 to implement theexample drone 202 of FIGS. 2 and/or 6. The processor platform 1200 ofthe illustrated example includes a processor implemented as the examplecontroller 610 of FIG. 6. The controller 610 of the illustrated exampleis hardware. For example, the controller 610 can be implemented by oneor more integrated circuit(s), logic circuit(s) or microprocessor(s)from any desired family or manufacturer. The hardware controller may bea semiconductor based (e.g., silicon based) device. The controller 610of the illustrated example implements the example prediction engine 616,the example route manager 618, the example calibrator 620, the examplelocation identifier 622, the example correlation grid identifier 624,the example service area identifier 626, and the example service areaselector 628 of FIG. 6.

The controller 610 of the illustrated example includes a local memory1202 (e.g., a cache). The controller 610 of the illustrated example isin communication with a main memory including a volatile memory 1204 anda non-volatile memory 1206 via a bus 1208. The volatile memory 1204 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1206 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1204,1206 is controlled by a memory controller. In the illustrated example ofFIG. 12, one or both of the volatile memory 1204 and/or the non-volatilememory 1206 implement(s) the example memory 614 of FIG. 6.

The processor platform 1200 of the illustrated example also includes auser interface circuit 1210. The user interface circuit 1210 may beimplemented by any type of interface standard, such as an Ethernetinterface, a universal serial bus (USB), and/or a PCI express interface.In the illustrated example, one or more input device(s) 630 areconnected to the user interface circuit 1210. The input device(s) 630permit(s) a user to enter data and commands into the controller 610. Theinput device(s) 630 can be implemented by, for example, an audio sensor,a camera (still or video), a keyboard, a button, a mouse, a touchscreen,a track-pad, a trackball, isopoint, a voice recognition system, amicrophone, and/or a liquid crystal display. One or more outputdevice(s) 632 are also connected to the user interface circuit 1210 ofthe illustrated example. The output device(s) 632 can be implemented,for example, by a light emitting diode, an organic light emitting diode,a liquid crystal display, a touchscreen and/or a speaker. The userinterface circuit 1210 of the illustrated example may, thus, include agraphics driver such as a graphics driver chip and/or processor. In theillustrated example, the input device(s) 630, the output device(s) 632,and the user interface circuit 1210 collectively implement the exampleuser interface 612 of FIG. 6.

The processor platform 1200 of the illustrated example also includes anetwork interface circuit 1212. The network interface circuit 1212 maybe implemented by any type of interface standard, such as an Ethernetinterface, a universal serial bus (USB), and/or a PCI express interface.In the illustrated example, the network interface circuit 1212 includesthe example GPS receiver 602, the example radio transmitter 604, theexample radio receiver 606, and the example antenna(s) 608 of FIG. 6 tofacilitate the exchange of data and/or signals with external machines(e.g., the server 218 of FIGS. 2 and/or 3, etc.) via a network 1214(e.g., a cellular network, a wireless local area network (WLAN), etc.).

Coded instructions 1218 including the coded instructions 1000 of FIG. 10may be stored in the local memory 1202, in the volatile memory 1204, inthe non-volatile memory 1206, and/or on a removable tangible computerreadable storage medium such as a flash memory stick, a CD, or a DVD.

From the foregoing, it will be appreciated that methods and apparatushave been disclosed for predicting favored wireless service areas fordrones. The disclosed methods and apparatus implement a model (e.g., amodel developed offline by a server) to predict favored wireless serviceareas for a drone traveling, or expected to be traveling, within anairspace. The model may be transmitted and/or uploaded to the droneprior to the drone entering the airspace or, alternatively, while thedrone is traveling within the airspace. The drone advantageouslyutilizes the favored wireless service areas identified by the model tomake, and/or to determine the need for, adjustments to the drone (e.g.,adjustments to a position and/or a route of the drone) in real timeduring a flight of the drone, and without the drone having to consumetime and/or processing resources that would otherwise be associated withmeasuring, obtaining, and/or evaluating reference signal strength dataassociated with the airspace in real time during flight.

In some examples, a controller for a drone is disclosed. In somedisclosed examples, the controller includes a service area identifier toidentify favored wireless service areas during a flight of the drone. Insome disclosed examples, the favored wireless service areas arepredicted by a model developed remotely from the drone. In somedisclosed examples, the controller further includes a service areaselector to select one of the favored wireless service areas during theflight. In some disclosed examples, the controller further includes aroute manager to adjust a flight path of the drone during the flightbased on the selected one of the favored wireless service areas.

In some disclosed examples, the controller further includes a locationidentifier to identify a location of the drone during the flight. Insome disclosed examples, the controller further includes a correlationgrid identifier to identify a correlation grid based on the location ofthe drone. In some disclosed examples, the favored wireless serviceareas are associated with the correlation grid.

In some disclosed examples, the drone is to receive the model from aserver in response to a request issued by the drone.

In some disclosed examples, the route manager is to adjust the flightpath of the drone by instructing the drone to move toward the selectedone of the favored wireless service areas. In some disclosed examples,the route manager is to adjust the drone by instructing the drone tomove a directional communication beam of a directional antenna of thedrone toward the selected one of the favored wireless service areas.

In some disclosed examples, the favored wireless service areas arepredicted by the model remotely from the drone based on reference signalstrength data obtained from sampling locations associated with a gridprior to the flight of the drone. In some disclosed examples, the gridis based on a three-dimensional representation of an airspace. In somedisclosed examples, the favored wireless service areas are predicted bythe model remotely from the drone based on a conditional random fieldprocess applied to a correlation grid selected from the grid.

In some disclosed examples, the service area identifier is to identifythe favored wireless service areas without the drone measuring referencesignal strength data during the flight of the drone.

In some examples, a non-transitory computer readable storage mediumcomprising instructions is disclosed. In some disclosed examples, theinstructions, when executed, cause one or more processors of a drone toidentify favored wireless service areas during a flight of the drone. Insome disclosed examples, the favored wireless service areas are based ona model developed by a server remote from the drone. In some disclosedexamples, the instructions, when executed, cause the one or moreprocessors of the drone to select one of the favored wireless serviceareas during the flight. In some disclosed examples, the instructions,when executed, cause the one or more processors of the drone to adjust adirection of the drone during the flight based on the selected one ofthe favored wireless service areas.

In some disclosed examples, the instructions, when executed, cause theone or more processors of the drone to identify a location of the droneduring the flight. In some disclosed examples, the instructions, whenexecuted, cause the one or more processors of the drone to identify acorrelation grid based on the location of the drone. In some disclosedexamples, the favored wireless service areas are associated with thecorrelation grid.

In some disclosed examples, the instructions, when executed, cause theone or more processors of the drone to issue a request for the model. Insome disclosed examples, the drone is to receive the model from theserver in response to the request.

In some disclosed examples, the instructions, when executed, cause theone or more processors of the drone to adjust the direction of the droneby instructing the drone to move toward the selected one of the favoredwireless service areas. In some disclosed examples, the instructions,when executed, cause the one or more processors of the drone to adjustthe direction of the drone by instructing the drone to move adirectional communication beam of a directional antenna of the dronetoward the selected one of the favored wireless service areas.

In some disclosed examples, the favored wireless service areas arepredicted by the model remotely from the drone based on reference signalstrength data obtained from sampling locations associated with a grid.In some disclosed examples, the grid is based on a three-dimensionalrepresentation of an airspace. In some disclosed examples, the favoredwireless service areas are predicted by the model remotely from thedrone based on a conditional random field process applied to acorrelation grid selected from the grid.

In some disclosed examples, the favored wireless service areas areidentified without the drone measuring reference signal strength dataduring the flight of the drone.

In some examples, a method is disclosed. In some disclosed examples, themethod includes identifying, by executing a computer readableinstruction with one or more processors of a drone, favored wirelessservice areas during a flight of the drone. In some disclosed examples,the favored wireless service areas are predicted by a model provided tothe drone via a network communication. In some disclosed examples, themethod further includes selecting, by executing a computer readableinstruction with the one or more processors, one of the favored wirelessservice areas during the flight based on the model. In some disclosedexamples, the method further includes adjusting, by executing a computerreadable instruction with the one or more processors, an operation ofthe drone during the flight based on the selected one of the favoredwireless service areas.

In some disclosed examples, the identifying the favored wireless serviceareas includes identifying a location of the drone during the flight. Insome disclosed examples, the wherein the identifying the favoredwireless service areas includes identifying a correlation grid based onthe location of the drone.

In some disclosed examples, the method further includes receiving themodel from a server in response to a request issued by the drone.

In some disclosed examples, the adjusting the operation of the droneincludes instructing the drone to move toward the selected one of thefavored wireless service areas. In some disclosed examples, theadjusting the operation of the drone includes instructing the drone tomove a directional communication beam of a directional antenna of thedrone toward the selected one of the favored wireless service areas.

In some examples, a controller for a drone is disclosed. In somedisclosed examples, the controller includes service area identifyingmeans to identify favored wireless service areas during a flight of thedrone. In some disclosed examples, the favored wireless service areasare predicted by a model developed remotely from the drone. In somedisclosed examples, the controller further includes service areaselecting means to select one of the favored wireless service areasduring the flight. In some disclosed examples, the controller furtherincludes route managing means to adjust the drone during the flightbased on the selected one of the favored wireless service areas.

In some disclosed examples, the controller further includes locationidentifying means to identify a location of the drone during the flight.In some disclosed examples, the controller further includes correlationgrid identifying means to identify a correlation grid based on thelocation of the drone.

In some disclosed examples, the route managing means is to adjust thedrone by instructing the drone to move toward the selected one of thefavored wireless service areas. In some disclosed examples, the routemanaging means is to adjust the drone by instructing the drone to move adirectional communication beam of a directional antenna of the dronetoward the selected one of the favored wireless service areas.

In some examples, a server is disclosed. In some disclosed examples, theserver comprises a database builder to build a database of favoredwireless service areas based on reference signal strength data sampledfrom a subset of grid locations from among a plurality of grid locationsof a grid corresponding to an airspace. In some disclosed examples, theserver further comprises a model developer to develop a model to predictfavored wireless service areas for the plurality of grid locations basedon the database. In some disclosed examples, the server furthercomprises a transmitter to transmit the model to a drone. In somedisclosed examples, the model is to be executed at the drone to identifyfavored wireless service areas during a flight of the drone.

In some disclosed examples, the transmitter is to transmit the model tothe drone in response to a request received at the server from thedrone. In some disclosed examples, the model enables the drone toidentify the favored wireless service areas without the drone measuringreference signal strength data during the flight of the drone.

In some disclosed examples, the server further includes a grid builderto build the grid based on a three-dimensional representation of theairspace. In some disclosed examples, the server further includes asampling location selector to select the subset of grid locations. Insome disclosed examples, the server further includes a sampler toinstruct one or more drones to collect the reference signal strengthdata from the subset of grid locations. In some disclosed examples, theserver further includes an evaluator to determine the favored wirelessservice areas for the subset of grid locations based on the referencesignal strength data.

In some disclosed examples, the server further includes a correlationgrid selector to select a correlation grid from the grid. In somedisclosed examples, the correlation grid has correlation grid locationsassociated with the grid. In some disclosed examples, the server furtherincludes a correlation grid evaluator to predict favored wirelessservice areas for the correlation grid locations.

In some examples, a non-transitory computer readable storage mediumcomprising instructions is disclosed. In some disclosed examples, theinstructions, when executed, cause one or more processors of a server tobuild a database of favored wireless service areas based on referencesignal strength data sampled from a subset of grid locations from amonga plurality of grid locations of a grid corresponding to an airspace. Insome disclosed examples, the instructions, when executed, cause the oneor more processors of the server to develop a model to predict favoredwireless service areas for the plurality of grid locations based on thedatabase. In some disclosed examples, the instructions, when executed,cause the one or more processors of the server to transmit the model toa drone. In some disclosed examples, the model is to be executed at thedrone to identify favored wireless service areas during a flight of thedrone.

In some disclosed examples, the instructions, when executed, cause theone or more processors of the server to transmit the model to the dronein response to a request received at the server from the drone. In somedisclosed examples, the instructions, when executed, cause the one ormore processors of the server to identify the favored wireless serviceareas without the drone measuring reference signal strength data duringthe flight of the drone.

In some disclosed examples, the instructions, when executed, cause theone or more processors of the server to build the grid based on athree-dimensional representation of the airspace. In some disclosedexamples, the instructions, when executed, cause the one or moreprocessors of the server to select the subset of grid locations. In somedisclosed examples, the instructions, when executed, cause the one ormore processors of the server to instruct one or more drones to collectthe reference signal strength data from the subset of grid locations. Insome disclosed examples, the instructions, when executed, cause the oneor more processors of the server to determine the favored wirelessservice areas for the subset of grid locations based on the referencesignal strength data.

In some disclosed examples, the instructions, when executed, cause theone or more processors of the server to select a correlation grid fromthe grid. In some disclosed examples, the correlation grid hascorrelation grid locations associated with the grid. In some disclosedexamples, the instructions, when executed, cause the one or moreprocessors of the server to predict favored wireless service areas forthe correlation grid locations.

In some examples, a server is disclosed. In some disclosed examples, theserver comprises database building means to build a database of favoredwireless service areas based on reference signal strength data sampledfrom a subset of grid locations from among a plurality of grid locationsof a grid corresponding to an airspace. In some disclosed examples, theserver further comprises model developing means to develop a model topredict favored wireless service areas for the plurality of gridlocations based on the database. In some disclosed examples, the serverfurther comprises transmitting means to transmit the model to a drone.In some disclosed examples, the model is to be executed at the drone toidentify favored wireless service areas during a flight of the drone.

In some disclosed examples, the transmitting means is to transmit themodel to the drone in response to a request received at the server fromthe drone. In some disclosed examples, the model enables the drone toidentify the favored wireless service areas without the drone measuringreference signal strength data during the flight of the drone.

In some disclosed examples, the server further includes grid buildingmeans to build the grid based on a three-dimensional representation ofthe airspace. In some disclosed examples, the server further includessampling location selecting means to select the subset of gridlocations. In some disclosed examples, the server further includessampling means to instruct one or more drones to collect the referencesignal strength data from the subset of grid locations. In somedisclosed examples, the server further includes evaluating means todetermine the favored wireless service areas for the subset of gridlocations based on the reference signal strength data.

In some disclosed examples, the server further includes correlation gridselecting means to select a correlation grid from the grid. In somedisclosed examples, the correlation grid has correlation grid locationsassociated with the grid. In some disclosed examples, the server furtherincludes correlation grid evaluating means to predict favored wirelessservice areas for the correlation grid locations.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A controller for a drone, the controllercomprising: a service area identifier to identify favored wirelessservice areas during a flight of the drone, the favored wireless serviceareas being predicted by a model developed remotely from the drone; aservice area selector to select one of the favored wireless serviceareas during the flight; and a route manager to adjust a flight path ofthe drone during the flight based on the selected one of the favoredwireless service areas.
 2. A controller as defined in claim 1, furtherincluding a location identifier to identify a location of the droneduring the flight.
 3. A controller as defined in claim 2, furtherincluding a correlation grid identifier to identify a correlation gridbased on the location of the drone.
 4. A controller as defined in claim3, wherein the favored wireless service areas are associated with thecorrelation grid.
 5. A controller as defined in claim 1, wherein thedrone is to receive the model from a server in response to a requestissued by the drone.
 6. A controller as defined in claim 1, wherein theroute manager is to adjust the flight path of the drone by instructingthe drone to move toward the selected one of the favored wirelessservice areas.
 7. A controller as defined in claim 1, wherein the routemanager is to adjust the drone by instructing the drone to move adirectional communication beam of a directional antenna of the dronetoward the selected one of the favored wireless service areas.
 8. Acontroller as defined in claim 1, wherein the favored wireless serviceareas are predicted by the model remotely from the drone based onreference signal strength data obtained from sampling locationsassociated with a grid prior to the flight of the drone, the grid beingbased on a three-dimensional representation of an airspace.
 9. Acontroller as defined in claim 8, wherein the favored wireless serviceareas are predicted by the model remotely from the drone based on aconditional random field process applied to a correlation grid selectedfrom the grid.
 10. A controller as defined in claim 1, wherein theservice area identifier is to identify the favored wireless serviceareas without the drone measuring reference signal strength data duringthe flight of the drone.
 11. A non-transitory computer readable storagemedium comprising instructions that, when executed, cause one or moreprocessors of a drone to at least: identify favored wireless serviceareas during a flight of the drone, the favored wireless service areasbased on a model developed by a server remote from the drone; select oneof the favored wireless service areas during the flight; and adjust adirection of the drone during the flight based on the selected one ofthe favored wireless service areas.
 12. A non-transitory computerreadable storage medium as defined in claim 11, wherein theinstructions, when executed, further cause the one or more processors ofthe drone to identify a location of the drone during the flight.
 13. Anon-transitory computer readable storage medium as defined in claim 12,wherein the instructions, when executed, further cause the one or moreprocessors of the drone to identify a correlation grid based on thelocation of the drone.
 14. A non-transitory computer readable storagemedium as defined in claim 13, wherein the favored wireless serviceareas are associated with the correlation grid.
 15. A non-transitorycomputer readable storage medium as defined in claim 11, wherein thefavored wireless service areas are predicted by the model remotely fromthe drone based on reference signal strength data obtained from samplinglocations associated with a grid, the grid being based on athree-dimensional representation of an airspace.
 16. A non-transitorycomputer readable storage medium as defined in claim 15, wherein thefavored wireless service areas are predicted by the model remotely fromthe drone based on a conditional random field process applied to acorrelation grid selected from the grid.
 17. A method, comprising:identifying, by executing a computer readable instruction with one ormore processors of a drone, favored wireless service areas during aflight of the drone, the favored wireless service areas being predictedby a model provided to the drone via a network communication; selecting,by executing a computer readable instruction with the one or moreprocessors, one of the favored wireless service areas during the flightbased on the model; and adjusting, by executing a computer readableinstruction with the one or more processors, an operation of the droneduring the flight based on the selected one of the favored wirelessservice areas.
 18. A method as defined in claim 17, wherein theidentifying the favored wireless service areas includes identifying alocation of the drone during the flight.
 19. A method as defined inclaim 18, wherein the identifying the favored wireless service areasincludes identifying a correlation grid based on the location of thedrone.
 20. A method as defined in claim 17, further including receivingthe model from a server in response to a request issued by the drone.